{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/code/is-image-file","entry":"is_image_file","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":157,"n_papers_ran":110,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":57,"n_samples_ran":22,"n_samples_fingerprinted":1,"n_places":163,"n_places_pointer_only":32,"by_status":{"ran_honours":0,"ran_violates":5,"ran_draft_wrong":0,"ran_fixture":0,"ran":17,"unverified":35},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2606.00738","paper":"/paper/arxiv-2606-00738","title":"SORA: Free Second-Order Attacks in Fast Adversarial Training","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"tmllab/2023_NeurIPS_AAER","path":"Imagenet-100/Imagenet.py","file_url":"https://github.com/tmllab/2023_NeurIPS_AAER/blob/HEAD/Imagenet-100/Imagenet.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ab4109634b75ef8b","mcp_get_code":{"code_sha256":"ab4109634b75ef8b"}},{"arxiv_id":"2602.01951","paper":"/paper/arxiv-2602-01951","title":"Enabling Progressive Whole-slide Image Analysis with Multi-scale Pyramidal Network","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"mahmoodlab/HIPT","path":"1-Hierarchical-Pretraining/eval_copy_detection.py","file_url":"https://github.com/mahmoodlab/HIPT/blob/HEAD/1-Hierarchical-Pretraining/eval_copy_detection.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"dfebfd56865b6379","mcp_get_code":{"code_sha256":"dfebfd56865b6379"}},{"arxiv_id":"2506.13277","paper":"/paper/seqpe-transformer-with-sequential-position","title":"SeqPE: Transformer with Sequential Position Encoding","date":"2025-06-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ghrua/seqpe","path":"image_seqpe/eval/prepare_imagenet_c.py","file_url":"https://github.com/ghrua/seqpe/blob/HEAD/image_seqpe/eval/prepare_imagenet_c.py","status":"ran_violates","verification_level":2,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"89c2cf3b97685684","mcp_get_code":{"code_sha256":"89c2cf3b97685684"}},{"arxiv_id":"2502.13987","paper":"/paper/selfage-personalized-facial-age","title":"SelfAge: Personalized Facial Age Transformation Using Self-reference Images","date":null,"month_inferred_from_arxiv_id":"2025-02","title_source":"archive","repo":"shiiiijp/selfage","path":"utils/data_utils.py","file_url":"https://github.com/shiiiijp/selfage/blob/HEAD/utils/data_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6f527bbe13c81b60","mcp_get_code":{"code_sha256":"6f527bbe13c81b60"}},{"arxiv_id":"2412.05186","paper":"/paper/one-shot-federated-learning-via-synthetic","title":"One-shot Federated Learning via Synthetic Distiller-Distillate Communication","date":"2024-12-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Carkham/FedSD2C","path":"preprocessing/Imagenette.py","file_url":"https://github.com/Carkham/FedSD2C/blob/HEAD/preprocessing/Imagenette.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"17595095b3e7dbf7","mcp_get_code":{"code_sha256":"17595095b3e7dbf7"}},{"arxiv_id":"2410.23905","paper":"/paper/text-difuse-an-interactive-multi-modal-image","title":"Text-DiFuse: An Interactive Multi-Modal Image Fusion Framework based on Text-modulated Diffusion Model","date":"2024-10-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Leiii-Cao/Text-DiFuse","path":"diffusion_fusion/util.py","file_url":"https://github.com/Leiii-Cao/Text-DiFuse/blob/HEAD/diffusion_fusion/util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"eb656fe08a6a70c4","mcp_get_code":{"code_sha256":"eb656fe08a6a70c4"}},{"arxiv_id":"2410.18472","paper":"/paper/what-if-the-input-is-expanded-in-ood","title":"What If the Input is Expanded in OOD Detection?","date":"2024-10-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tmlr-group/cover","path":"utils/imagenet_c/make_imagenet_c.py","file_url":"https://github.com/tmlr-group/cover/blob/HEAD/utils/imagenet_c/make_imagenet_c.py","status":"ran_violates","verification_level":2,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"89c2cf3b97685684","mcp_get_code":{"code_sha256":"89c2cf3b97685684"}},{"arxiv_id":"2410.11215","paper":"/paper/a-clip-powered-framework-for-robust-and","title":"A CLIP-Powered Framework for Robust and Generalizable Data Selection","date":"2024-10-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Jackbrocp/clip-powered-data-selection","path":"dataset.py","file_url":"https://github.com/Jackbrocp/clip-powered-data-selection/blob/HEAD/dataset.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"13976a63c7d68a7f","mcp_get_code":{"code_sha256":"13976a63c7d68a7f"}},{"arxiv_id":"2410.07149","paper":"/paper/towards-interpreting-visual-information","title":"Towards Interpreting Visual Information Processing in Vision-Language Models","date":"2024-10-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"clemneo/llava-interp","path":"scripts/logit_lens/create_logit_lens.py","file_url":"https://github.com/clemneo/llava-interp/blob/HEAD/scripts/logit_lens/create_logit_lens.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8fd54a61b8fcee04","mcp_get_code":{"code_sha256":"8fd54a61b8fcee04"}},{"arxiv_id":"2410.00379","paper":"/paper/cxpmrg-bench-pre-training-and-benchmarking","title":"CXPMRG-Bench: Pre-training and Benchmarking for X-ray Medical Report Generation on CheXpert Plus Dataset","date":"2024-10-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"event-ahu/medical_image_analysis","path":"AM_MRG/SwinCheX/models/custom_image_folder.py","file_url":"https://github.com/event-ahu/medical_image_analysis/blob/HEAD/AM_MRG/SwinCheX/models/custom_image_folder.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"c7497af4b6ddc418","mcp_get_code":{"code_sha256":"c7497af4b6ddc418"}},{"arxiv_id":"2409.12191","paper":"/paper/qwen2-vl-enhancing-vision-language-model-s","title":"Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution","date":"2024-09-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yangyucheng000/University","path":"JDRL-mindspore/dataset_RGB.py","file_url":"https://github.com/yangyucheng000/University/blob/HEAD/JDRL-mindspore/dataset_RGB.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"91fca7a2b44569ad","mcp_get_code":{"code_sha256":"91fca7a2b44569ad"}},{"arxiv_id":"2409.08248","paper":"/paper/textboost-towards-one-shot-personalization-of","title":"TextBoost: Towards One-Shot Personalization of Text-to-Image Models via Fine-tuning Text Encoder","date":"2024-09-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nahyeonkaty/textboost","path":"src/textboost/datasets.py","file_url":"https://github.com/nahyeonkaty/textboost/blob/HEAD/src/textboost/datasets.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"72fcd818096aee74","mcp_get_code":{"code_sha256":"72fcd818096aee74"}},{"arxiv_id":"2408.11480","paper":"/paper/oapt-offset-aware-partition-transformer-for","title":"OAPT: Offset-Aware Partition Transformer for Double JPEG Artifacts Removal","date":"2024-08-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"QMoQ/OAPT","path":"oapt/models/utils_image.py","file_url":"https://github.com/QMoQ/OAPT/blob/HEAD/oapt/models/utils_image.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"226f6afcd57ff476","mcp_get_code":{"code_sha256":"226f6afcd57ff476"}},{"arxiv_id":"2407.14949","paper":"/paper/cocog-2-controllable-generation-of-visual","title":"CoCoG-2: Controllable generation of visual stimuli for understanding human concept representation","date":"2024-07-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ncclab-sustech/cocog-2","path":"dataset.py","file_url":"https://github.com/ncclab-sustech/cocog-2/blob/HEAD/dataset.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0ae9b5b39db5b9c0","mcp_get_code":{"code_sha256":"0ae9b5b39db5b9c0"}},{"arxiv_id":"2407.09842","paper":"/paper/eliminating-feature-ambiguity-for-few-shot","title":"Eliminating Feature Ambiguity for Few-Shot Segmentation","date":"2024-07-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sam1224/aenet","path":"HDMNet/util/dataset.py","file_url":"https://github.com/sam1224/aenet/blob/HEAD/HDMNet/util/dataset.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"0684a938c70bf564","mcp_get_code":{"code_sha256":"0684a938c70bf564"}},{"arxiv_id":"2407.07544","paper":"/paper/disentangling-masked-autoencoders-for","title":"Disentangling Masked Autoencoders for Unsupervised Domain Generalization","date":"2024-07-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rookiehb/dismae","path":"datasets.py","file_url":"https://github.com/rookiehb/dismae/blob/HEAD/datasets.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"336526c0f00f687c","mcp_get_code":{"code_sha256":"336526c0f00f687c"}},{"arxiv_id":"2406.18516","paper":"/paper/denoising-as-adaptation-noise-space-domain","title":"Denoising as Adaptation: Noise-Space Domain Adaptation for Image Restoration","date":"2024-06-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kangliao929/noise-da","path":"core/base_dataset.py","file_url":"https://github.com/kangliao929/noise-da/blob/HEAD/core/base_dataset.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ab4109634b75ef8b","mcp_get_code":{"code_sha256":"ab4109634b75ef8b"}},{"arxiv_id":"2406.18051","paper":"/paper/vit-1-58b-mobile-vision-transformers-in-the-1","title":"ViT-1.58b: Mobile Vision Transformers in the 1-bit Era","date":"2024-06-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dlyuangod/vit-1.58b","path":"dataset_floder.py","file_url":"https://github.com/dlyuangod/vit-1.58b/blob/HEAD/dataset_floder.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8ac50ae8773b9431","mcp_get_code":{"code_sha256":"8ac50ae8773b9431"}},{"arxiv_id":"2406.09135","paper":"/paper/adarevd-adaptive-patch-exiting-reversible-1","title":"AdaRevD: Adaptive Patch Exiting Reversible Decoder Pushes the Limit of Image Deblurring","date":"2024-06-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"invokerer/deeprft","path":"dataset_RGB.py","file_url":"https://github.com/invokerer/deeprft/blob/HEAD/dataset_RGB.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"91fca7a2b44569ad","mcp_get_code":{"code_sha256":"91fca7a2b44569ad"}},{"arxiv_id":"2405.16262","paper":"/paper/layer-aware-analysis-of-catastrophic","title":"Layer-Aware Analysis of Catastrophic Overfitting: Revealing the Pseudo-Robust Shortcut Dependency","date":"2024-05-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tmllab/2024_ICML_LAP","path":"Tiny-imagenet/tiny_imagenet.py","file_url":"https://github.com/tmllab/2024_ICML_LAP/blob/HEAD/Tiny-imagenet/tiny_imagenet.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ab4109634b75ef8b","mcp_get_code":{"code_sha256":"ab4109634b75ef8b"}},{"arxiv_id":"2405.03649","paper":"/paper/learning-robust-classifiers-with-self-guided","title":"Learning Robust Classifiers with Self-Guided Spurious Correlation Mitigation","date":"2024-05-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gtzheng/LBC","path":"datasets/in9_data.py","file_url":"https://github.com/gtzheng/LBC/blob/HEAD/datasets/in9_data.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9c67275dc5e1daaf","mcp_get_code":{"code_sha256":"9c67275dc5e1daaf"}},{"arxiv_id":"2404.12391","paper":"/paper/on-the-content-bias-in-frechet-video-distance","title":"On the Content Bias in Fréchet Video Distance","date":"2024-04-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"songweige/tats","path":"tats/data.py","file_url":"https://github.com/songweige/tats/blob/HEAD/tats/data.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"59848503040bbdac","mcp_get_code":{"code_sha256":"59848503040bbdac"}},{"arxiv_id":"2404.10575","paper":"/paper/emc-2-efficient-mcmc-negative-sampling-for","title":"EMC$^2$: Efficient MCMC Negative Sampling for Contrastive Learning with Global Convergence","date":"2024-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"amazon-science/contrastive_emc2","path":"folder.py","file_url":"https://github.com/amazon-science/contrastive_emc2/blob/HEAD/folder.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d9fa6dde50b3f702","mcp_get_code":{"code_sha256":"d9fa6dde50b3f702"}},{"arxiv_id":"2404.09732","paper":"/paper/photo-realistic-image-restoration-in-the-wild","title":"Photo-Realistic Image Restoration in the Wild with Controlled Vision-Language Models","date":"2024-04-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"algolzw/daclip-uir","path":"da-clip/src/evaluate.py","file_url":"https://github.com/algolzw/daclip-uir/blob/HEAD/da-clip/src/evaluate.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"49c770cc5e58f787","mcp_get_code":{"code_sha256":"49c770cc5e58f787"}},{"arxiv_id":"2404.08154","paper":"/paper/eliminating-catastrophic-overfitting-via-1","title":"Eliminating Catastrophic Overfitting Via Abnormal Adversarial Examples Regularization","date":"2024-04-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tmllab/2023_neurips_aaer","path":"Imagenet-100/Imagenet.py","file_url":"https://github.com/tmllab/2023_neurips_aaer/blob/HEAD/Imagenet-100/Imagenet.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ab4109634b75ef8b","mcp_get_code":{"code_sha256":"ab4109634b75ef8b"}},{"arxiv_id":"2403.17695","paper":"/paper/plainmamba-improving-non-hierarchical-mamba","title":"PlainMamba: Improving Non-Hierarchical Mamba in Visual Recognition","date":"2024-03-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"karl1109/scsegamba","path":"datasets/image_folder.py","file_url":"https://github.com/karl1109/scsegamba/blob/HEAD/datasets/image_folder.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ab4109634b75ef8b","mcp_get_code":{"code_sha256":"ab4109634b75ef8b"}},{"arxiv_id":"2403.06946","paper":"/paper/split-to-merge-unifying-separated-modalities","title":"Split to Merge: Unifying Separated Modalities for Unsupervised Domain Adaptation","date":"2024-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"TL-UESTC/UniMoS","path":"utils/build.py","file_url":"https://github.com/TL-UESTC/UniMoS/blob/HEAD/utils/build.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ab4109634b75ef8b","mcp_get_code":{"code_sha256":"ab4109634b75ef8b"}},{"arxiv_id":"2403.01189","paper":"/paper/training-unbiased-diffusion-models-from","title":"Training Unbiased Diffusion Models From Biased Dataset","date":"2024-03-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ermongroup/fairgen","path":"src/KL-BigGAN/datasets.py","file_url":"https://github.com/ermongroup/fairgen/blob/HEAD/src/KL-BigGAN/datasets.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4e6a8faaf8e44abe","mcp_get_code":{"code_sha256":"4e6a8faaf8e44abe"}},{"arxiv_id":"2402.18078","paper":"/paper/coarse-to-fine-latent-diffusion-for-pose","title":"Coarse-to-Fine Latent Diffusion for Pose-Guided Person Image Synthesis","date":"2024-02-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YanzuoLu/CFLD","path":"generate_fashion_datasets.py","file_url":"https://github.com/YanzuoLu/CFLD/blob/HEAD/generate_fashion_datasets.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ab4109634b75ef8b","mcp_get_code":{"code_sha256":"ab4109634b75ef8b"}},{"arxiv_id":"2401.16352","paper":"/paper/adversarial-training-on-purification-atop","title":"Adversarial Training on Purification (AToP): Advancing Both Robustness and Generalization","date":"2024-01-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"glin2022/atop","path":"utils/data.py","file_url":"https://github.com/glin2022/atop/blob/HEAD/utils/data.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1fabc732ffcd8750","mcp_get_code":{"code_sha256":"1fabc732ffcd8750"}},{"arxiv_id":"2401.03379","paper":"/paper/towards-effective-multiple-in-one-image","title":"Towards Effective Multiple-in-One Image Restoration: A Sequential and Prompt Learning Strategy","date":"2024-01-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xiangtaokong/mioir","path":"data_script/gen_sub.py","file_url":"https://github.com/xiangtaokong/mioir/blob/HEAD/data_script/gen_sub.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ab4109634b75ef8b","mcp_get_code":{"code_sha256":"ab4109634b75ef8b"}},{"arxiv_id":"2312.16272","paper":"/paper/ssr-encoder-encoding-selective-subject","title":"SSR-Encoder: Encoding Selective Subject Representation for Subject-Driven Generation","date":"2023-12-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Xiaojiu-z/SSR_Encoder","path":"infer_controlnet.py","file_url":"https://github.com/Xiaojiu-z/SSR_Encoder/blob/HEAD/infer_controlnet.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2383223cfe77b76c","mcp_get_code":{"code_sha256":"2383223cfe77b76c"}},{"arxiv_id":"2312.11121","paper":"/paper/multi-scale-reconstruction-of-turbulent","title":"Multi-scale Reconstruction of Turbulent Rotating Flows with Generative Diffusion Models","date":null,"month_inferred_from_arxiv_id":"2023-12","title_source":"archive","repo":"smartturb/palette-turb","path":"core/base_dataset.py","file_url":"https://github.com/smartturb/palette-turb/blob/HEAD/core/base_dataset.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ab4109634b75ef8b","mcp_get_code":{"code_sha256":"ab4109634b75ef8b"}},{"arxiv_id":"2312.10376","paper":"/paper/sa-2-vp-spatially-aligned-and-adapted-visual","title":"SA$^2$VP: Spatially Aligned-and-Adapted Visual Prompt","date":"2023-12-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tommy-xq/sa2vp","path":"dataset_folder.py","file_url":"https://github.com/tommy-xq/sa2vp/blob/HEAD/dataset_folder.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8ac50ae8773b9431","mcp_get_code":{"code_sha256":"8ac50ae8773b9431"}},{"arxiv_id":"2312.07330","paper":"/paper/learned-representation-guided-diffusion","title":"Learned representation-guided diffusion models for large-image generation","date":"2023-12-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cvlab-stonybrook/large-image-diffusion","path":"ldm/modules/image_degradation/utils_image.py","file_url":"https://github.com/cvlab-stonybrook/large-image-diffusion/blob/HEAD/ldm/modules/image_degradation/utils_image.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"226f6afcd57ff476","mcp_get_code":{"code_sha256":"226f6afcd57ff476"}},{"arxiv_id":"2312.02517","paper":"/paper/simplifying-neural-network-training-under-1","title":"Simplifying Neural Network Training Under Class Imbalance","date":"2023-12-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ravidziv/SimplifyingImbalancedTraining","path":"imbalanced/camvid.py","file_url":"https://github.com/ravidziv/SimplifyingImbalancedTraining/blob/HEAD/imbalanced/camvid.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"af447e65a04b459e","mcp_get_code":{"code_sha256":"af447e65a04b459e"}},{"arxiv_id":"2312.02153","paper":"/paper/aligning-and-prompting-everything-all-at-once","title":"Aligning and Prompting Everything All at Once for Universal Visual Perception","date":"2023-12-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"earth-insights/ClassTrans","path":"src/dataset/utils.py","file_url":"https://github.com/earth-insights/ClassTrans/blob/HEAD/src/dataset/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"25fea6c1ac82a8c5","mcp_get_code":{"code_sha256":"25fea6c1ac82a8c5"}},{"arxiv_id":"2311.17626","paper":"/paper/focus-on-query-adversarial-mining-transformer-1","title":"Focus on Query: Adversarial Mining Transformer for Few-Shot Segmentation","date":"2023-11-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Wyxdm/AMNet","path":"util/dataset.py","file_url":"https://github.com/Wyxdm/AMNet/blob/HEAD/util/dataset.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0684a938c70bf564","mcp_get_code":{"code_sha256":"0684a938c70bf564"}},{"arxiv_id":"2310.19011","paper":"/paper/efficient-test-time-adaptation-for-super-1","title":"Efficient Test-Time Adaptation for Super-Resolution with Second-Order Degradation and Reconstruction","date":"2023-10-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dengzeshuai/srtta","path":"src/utils/utils_image.py","file_url":"https://github.com/dengzeshuai/srtta/blob/HEAD/src/utils/utils_image.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"226f6afcd57ff476","mcp_get_code":{"code_sha256":"226f6afcd57ff476"}},{"arxiv_id":"2310.17594","paper":"/paper/spa-a-graph-spectral-alignment-perspective","title":"SPA: A Graph Spectral Alignment Perspective for Domain Adaptation","date":"2023-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"CrownX/SPA","path":"code/utils.py","file_url":"https://github.com/CrownX/SPA/blob/HEAD/code/utils.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ab4109634b75ef8b","mcp_get_code":{"code_sha256":"ab4109634b75ef8b"}},{"arxiv_id":"2310.17316","paper":"/paper/defect-spectrum-a-granular-look-of-large","title":"Defect Spectrum: A Granular Look of Large-Scale Defect Datasets with Rich Semantics","date":"2023-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"EnVision-Research/Defect_Spectrum","path":"dataset/base_dataset.py","file_url":"https://github.com/EnVision-Research/Defect_Spectrum/blob/HEAD/dataset/base_dataset.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"6f527bbe13c81b60","mcp_get_code":{"code_sha256":"6f527bbe13c81b60"}},{"arxiv_id":"2310.07492","paper":"/paper/boosting-black-box-attack-to-deep-neural","title":"Boosting Black-box Attack to Deep Neural Networks with Conditional Diffusion Models","date":"2023-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ryliu68/CDMA","path":"core/base_dataset.py","file_url":"https://github.com/ryliu68/CDMA/blob/HEAD/core/base_dataset.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ab4109634b75ef8b","mcp_get_code":{"code_sha256":"ab4109634b75ef8b"}},{"arxiv_id":"2310.05397","paper":"/paper/find-your-optimal-assignments-on-the-fly-a","title":"Find Your Optimal Assignments On-the-fly: A Holistic Framework for Clustered Federated Learning","date":"2023-10-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LINs-lab/HCFL","path":"create_c/make_imagenet_64_c.py","file_url":"https://github.com/LINs-lab/HCFL/blob/HEAD/create_c/make_imagenet_64_c.py","status":"ran_violates","verification_level":2,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"89c2cf3b97685684","mcp_get_code":{"code_sha256":"89c2cf3b97685684"}},{"arxiv_id":"2310.01018","paper":"/paper/controlling-vision-language-models-for","title":"Controlling Vision-Language Models for Multi-Task Image Restoration","date":"2023-10-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Algolzw/daclip-uir","path":"da-clip/src/evaluate.py","file_url":"https://github.com/Algolzw/daclip-uir/blob/HEAD/da-clip/src/evaluate.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"49c770cc5e58f787","mcp_get_code":{"code_sha256":"49c770cc5e58f787"}},{"arxiv_id":"2309.04089","paper":"/paper/toward-sufficient-spatial-frequency","title":"Toward Sufficient Spatial-Frequency Interaction for Gradient-aware Underwater Image Enhancement","date":"2023-09-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhihefang/SFGNet","path":"dataset.py","file_url":"https://github.com/zhihefang/SFGNet/blob/HEAD/dataset.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e731b9c5e327d08b","mcp_get_code":{"code_sha256":"e731b9c5e327d08b"}},{"arxiv_id":"2309.03063","paper":"/paper/prompt-based-all-in-one-image-restoration","title":"Prompt-based Ingredient-Oriented All-in-One Image Restoration","date":"2023-09-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Tombs98/CAPTNet","path":"dataset_RGB.py","file_url":"https://github.com/Tombs98/CAPTNet/blob/HEAD/dataset_RGB.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"91fca7a2b44569ad","mcp_get_code":{"code_sha256":"91fca7a2b44569ad"}},{"arxiv_id":"2309.03020","paper":"/paper/seal-a-framework-for-systematic-evaluation-of","title":"SEAL: A Framework for Systematic Evaluation of Real-World Super-Resolution","date":"2023-09-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xpixelgroup/seal","path":"seal/models/utils_image.py","file_url":"https://github.com/xpixelgroup/seal/blob/HEAD/seal/models/utils_image.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"226f6afcd57ff476","mcp_get_code":{"code_sha256":"226f6afcd57ff476"}},{"arxiv_id":"2308.04417","paper":"/paper/diffcr-a-fast-conditional-diffusion-framework","title":"DiffCR: A Fast Conditional Diffusion Framework for Cloud Removal from Optical Satellite Images","date":"2023-08-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xavierjiezou/diffcr","path":"core/base_dataset.py","file_url":"https://github.com/xavierjiezou/diffcr/blob/HEAD/core/base_dataset.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ab4109634b75ef8b","mcp_get_code":{"code_sha256":"ab4109634b75ef8b"}},{"arxiv_id":"2308.03867","paper":"/paper/from-sky-to-the-ground-a-large-scale","title":"From Sky to the Ground: A Large-scale Benchmark and Simple Baseline Towards Real Rain Removal","date":"2023-08-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yunguo224/lhp-rain","path":"SCD-Former/dataset/dataset_denoise.py","file_url":"https://github.com/yunguo224/lhp-rain/blob/HEAD/SCD-Former/dataset/dataset_denoise.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"91fca7a2b44569ad","mcp_get_code":{"code_sha256":"91fca7a2b44569ad"}},{"arxiv_id":"2308.02228","paper":"/paper/painterly-image-harmonization-using-diffusion","title":"Painterly Image Harmonization using Diffusion Model","date":"2023-08-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bcmi/phdiffusion-painterly-image-harmonization","path":"ldm/modules/image_degradation/utils_image.py","file_url":"https://github.com/bcmi/phdiffusion-painterly-image-harmonization/blob/HEAD/ldm/modules/image_degradation/utils_image.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"226f6afcd57ff476","mcp_get_code":{"code_sha256":"226f6afcd57ff476"}},{"arxiv_id":"2307.15139","paper":"/paper/online-clustered-codebook","title":"Online Clustered Codebook","date":"2023-07-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lyndonzheng/cvq-vae","path":"image_folder.py","file_url":"https://github.com/lyndonzheng/cvq-vae/blob/HEAD/image_folder.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ab4109634b75ef8b","mcp_get_code":{"code_sha256":"ab4109634b75ef8b"}},{"arxiv_id":"2306.16699","paper":"/paper/rapid-inr-storage-efficient-cpu-free-dnn","title":"Rapid-INR: Storage Efficient CPU-free DNN Training Using Implicit Neural Representation","date":"2023-06-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sharc-lab/rapid-inr","path":"INR_encoding.py","file_url":"https://github.com/sharc-lab/rapid-inr/blob/HEAD/INR_encoding.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2383223cfe77b76c","mcp_get_code":{"code_sha256":"2383223cfe77b76c"}},{"arxiv_id":"2305.01644","paper":"/paper/key-locked-rank-one-editing-for-text-to-image","title":"Key-Locked Rank One Editing for Text-to-Image Personalization","date":"2023-05-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ChenDarYen/Key-Locked-Rank-One-Editing-for-Text-to-Image-Personalization","path":"ldm/modules/image_degradation/utils_image.py","file_url":"https://github.com/ChenDarYen/Key-Locked-Rank-One-Editing-for-Text-to-Image-Personalization/blob/HEAD/ldm/modules/image_degradation/utils_image.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"226f6afcd57ff476","mcp_get_code":{"code_sha256":"226f6afcd57ff476"}},{"arxiv_id":"2305.00348","paper":"/paper/modality-invariant-visual-odometry-for","title":"Modality-invariant Visual Odometry for Embodied Vision","date":"2023-04-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"memmelma/VO-Transformer","path":"pointnav_vo/model_utils/mmae/dataset_folder.py","file_url":"https://github.com/memmelma/VO-Transformer/blob/HEAD/pointnav_vo/model_utils/mmae/dataset_folder.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"1d15a850090b3c20","mcp_get_code":{"code_sha256":"1d15a850090b3c20"}},{"arxiv_id":"2304.13742","paper":"/paper/tr0n-translator-networks-for-0-shot-plug-and","title":"TR0N: Translator Networks for 0-Shot Plug-and-Play Conditional Generation","date":"2023-04-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gnobitab/FuseDream","path":"BigGAN_utils/datasets.py","file_url":"https://github.com/gnobitab/FuseDream/blob/HEAD/BigGAN_utils/datasets.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4e6a8faaf8e44abe","mcp_get_code":{"code_sha256":"4e6a8faaf8e44abe"}},{"arxiv_id":"2303.13062","paper":"/paper/siedob-semantic-image-editing-by","title":"SIEDOB: Semantic Image Editing by Disentangling Object and Background","date":"2023-03-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"WuyangLuo/SIEDOB","path":"background/utils.py","file_url":"https://github.com/WuyangLuo/SIEDOB/blob/HEAD/background/utils.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ab4109634b75ef8b","mcp_get_code":{"code_sha256":"ab4109634b75ef8b"}},{"arxiv_id":"2303.00748","paper":"/paper/efficient-and-explicit-modelling-of-image","title":"Efficient and Explicit Modelling of Image Hierarchies for Image Restoration","date":"2023-03-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ofsoundof/GRL-Image-Restoration","path":"utils/utils_bsr/utils_image.py","file_url":"https://github.com/ofsoundof/GRL-Image-Restoration/blob/HEAD/utils/utils_bsr/utils_image.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"226f6afcd57ff476","mcp_get_code":{"code_sha256":"226f6afcd57ff476"}},{"arxiv_id":"2303.00748","paper":"/paper/efficient-and-explicit-modelling-of-image","title":"Efficient and Explicit Modelling of Image Hierarchies for Image Restoration","date":"2023-03-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ofsoundof/GRL-Image-Restoration","path":"utils/image_utils.py","file_url":"https://github.com/ofsoundof/GRL-Image-Restoration/blob/HEAD/utils/image_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d8709f460c3945ff","mcp_get_code":{"code_sha256":"d8709f460c3945ff"}},{"arxiv_id":"2302.11984","paper":"/paper/unsupervised-domain-adaptation-via-distilled","title":"Unsupervised Domain Adaptation via Distilled Discriminative Clustering","date":"2023-02-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huitangtang/disclusterda","path":"utils/folder.py","file_url":"https://github.com/huitangtang/disclusterda/blob/HEAD/utils/folder.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2d2234ff68f9f460","mcp_get_code":{"code_sha256":"2d2234ff68f9f460"}},{"arxiv_id":"2301.12689","paper":"/paper/edge-guided-multi-domain-rgb-to-tir-image","title":"Edge-guided Multi-domain RGB-to-TIR image Translation for Training Vision Tasks with Challenging Labels","date":"2023-01-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rpmsnu/srgb-tir","path":"data.py","file_url":"https://github.com/rpmsnu/srgb-tir/blob/HEAD/data.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ab4109634b75ef8b","mcp_get_code":{"code_sha256":"ab4109634b75ef8b"}},{"arxiv_id":"2210.06780","paper":"/paper/intermediate-prototype-mining-transformer-for","title":"Intermediate Prototype Mining Transformer for Few-Shot Semantic Segmentation","date":"2022-10-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LIUYUANWEI98/IPMT","path":"util/dataset.py","file_url":"https://github.com/LIUYUANWEI98/IPMT/blob/HEAD/util/dataset.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0684a938c70bf564","mcp_get_code":{"code_sha256":"0684a938c70bf564"}},{"arxiv_id":"2209.12699","paper":"/paper/accurate-and-efficient-stereo-matching-via","title":"Accurate and Efficient Stereo Matching via Attention Concatenation Volume","date":"2022-09-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gangweix/fast-acvnet","path":"datasets/MiddleburyLoader.py","file_url":"https://github.com/gangweix/fast-acvnet/blob/HEAD/datasets/MiddleburyLoader.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ab4109634b75ef8b","mcp_get_code":{"code_sha256":"ab4109634b75ef8b"}},{"arxiv_id":"2209.04996","paper":"/paper/switchable-online-knowledge-distillation","title":"Switchable Online Knowledge Distillation","date":"2022-09-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hfutqian/SwitOKD","path":"SwitOKD_code/Tiny-ImageNet/Train/SwitOKD/tiny_imagenet.py","file_url":"https://github.com/hfutqian/SwitOKD/blob/HEAD/SwitOKD_code/Tiny-ImageNet/Train/SwitOKD/tiny_imagenet.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ab4109634b75ef8b","mcp_get_code":{"code_sha256":"ab4109634b75ef8b"}},{"arxiv_id":"2208.07049","paper":"/paper/self-supervised-vision-transformers-for","title":"Self-Supervised Vision Transformers for Malware Detection","date":"2022-08-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sachith500/sherlock","path":"dataset_folder.py","file_url":"https://github.com/sachith500/sherlock/blob/HEAD/dataset_folder.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8ac50ae8773b9431","mcp_get_code":{"code_sha256":"8ac50ae8773b9431"}},{"arxiv_id":"2207.13048","paper":"/paper/domain-adaptation-under-open-set-label-shift","title":"Domain Adaptation under Open Set Label Shift","date":"2022-07-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VisionLearningGroup/DANCE","path":"data_loader/mydataset.py","file_url":"https://github.com/VisionLearningGroup/DANCE/blob/HEAD/data_loader/mydataset.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ab4109634b75ef8b","mcp_get_code":{"code_sha256":"ab4109634b75ef8b"}},{"arxiv_id":"2207.05312","paper":"/paper/outpainting-by-queries","title":"Outpainting by Queries","date":"2022-07-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Kaiseem/QueryOTR","path":"datasets.py","file_url":"https://github.com/Kaiseem/QueryOTR/blob/HEAD/datasets.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"719cc078ef36535e","mcp_get_code":{"code_sha256":"719cc078ef36535e"}},{"arxiv_id":"2206.09479","paper":"/paper/studiogan-a-taxonomy-and-benchmark-of-gans","title":"StudioGAN: A Taxonomy and Benchmark of GANs for Image Synthesis","date":"2022-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lyqcom/biggan","path":"src/datasets.py","file_url":"https://github.com/lyqcom/biggan/blob/HEAD/src/datasets.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"39565434228935b8","mcp_get_code":{"code_sha256":"39565434228935b8"}},{"arxiv_id":"2205.15677","paper":"/paper/augmentation-aware-self-supervision-for-data-1","title":"Augmentation-Aware Self-Supervision for Data-Efficient GAN Training","date":"2022-05-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liang-hou/augself-gan","path":"augself-biggan/datasets.py","file_url":"https://github.com/liang-hou/augself-gan/blob/HEAD/augself-biggan/datasets.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4e6a8faaf8e44abe","mcp_get_code":{"code_sha256":"4e6a8faaf8e44abe"}},{"arxiv_id":"2205.05675","paper":"/paper/ntire-2022-challenge-on-efficient-super","title":"NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results","date":"2022-05-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ofsoundof/imdn","path":"utils/utils_image.py","file_url":"https://github.com/ofsoundof/imdn/blob/HEAD/utils/utils_image.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ab4109634b75ef8b","mcp_get_code":{"code_sha256":"ab4109634b75ef8b"}},{"arxiv_id":"2205.03892","paper":"/paper/convmae-masked-convolution-meets-masked","title":"ConvMAE: Masked Convolution Meets Masked Autoencoders","date":"2022-05-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mx-mark/dmjd","path":"util/dataset_folder.py","file_url":"https://github.com/mx-mark/dmjd/blob/HEAD/util/dataset_folder.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8ac50ae8773b9431","mcp_get_code":{"code_sha256":"8ac50ae8773b9431"}},{"arxiv_id":"2205.01823","paper":"/paper/symmetry-and-uncertainty-aware-object-slam","title":"Symmetry and Uncertainty-Aware Object SLAM for 6DoF Object Pose Estimation","date":null,"month_inferred_from_arxiv_id":"2022-05","title_source":"archive","repo":"rpng/suo_slam","path":"lib/datasets/bop.py","file_url":"https://github.com/rpng/suo_slam/blob/HEAD/lib/datasets/bop.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9f8ce0fb894edc32","mcp_get_code":{"code_sha256":"9f8ce0fb894edc32"}},{"arxiv_id":"2204.08381","paper":"/paper/multiple-environment-self-adaptive-network","title":"Multiple-environment Self-adaptive Network for Aerial-view Geo-localization","date":"2022-04-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"layumi/University1652-Baseline","path":"folder.py","file_url":"https://github.com/layumi/University1652-Baseline/blob/HEAD/folder.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8ac50ae8773b9431","mcp_get_code":{"code_sha256":"8ac50ae8773b9431"}},{"arxiv_id":"2203.16521","paper":"/paper/coordgan-self-supervised-dense","title":"CoordGAN: Self-Supervised Dense Correspondences Emerge from GANs","date":"2022-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"NVlabs/CoordGAN","path":"dataset.py","file_url":"https://github.com/NVlabs/CoordGAN/blob/HEAD/dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"08750a49987bd9a5","mcp_get_code":{"code_sha256":"08750a49987bd9a5"}},{"arxiv_id":"2203.15375","paper":"/paper/a-style-aware-discriminator-for-controllable","title":"A Style-aware Discriminator for Controllable Image Translation","date":"2022-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kunheek/style-aware-discriminator","path":"mylib/misc.py","file_url":"https://github.com/kunheek/style-aware-discriminator/blob/HEAD/mylib/misc.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1fe2d3a3a4d4a3a9","mcp_get_code":{"code_sha256":"1fe2d3a3a4d4a3a9"}},{"arxiv_id":"2203.14415","paper":"/paper/mugs-a-multi-granular-self-supervised","title":"Mugs: A Multi-Granular Self-Supervised Learning Framework","date":"2022-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sail-sg/mugs","path":"src/dataset.py","file_url":"https://github.com/sail-sg/mugs/blob/HEAD/src/dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"75aa69b9b8b80e72","mcp_get_code":{"code_sha256":"75aa69b9b8b80e72"}},{"arxiv_id":"2203.13278","paper":"/paper/practical-blind-denoising-via-swin-conv-unet","title":"Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis","date":"2022-03-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cszn/scunet","path":"utils/utils_image.py","file_url":"https://github.com/cszn/scunet/blob/HEAD/utils/utils_image.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"226f6afcd57ff476","mcp_get_code":{"code_sha256":"226f6afcd57ff476"}},{"arxiv_id":"2203.12691","paper":"/paper/learning-to-generate-line-drawings-that","title":"Learning to generate line drawings that convey geometry and semantics","date":"2022-03-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"carolineec/informative-drawings","path":"dataset.py","file_url":"https://github.com/carolineec/informative-drawings/blob/HEAD/dataset.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"59848503040bbdac","mcp_get_code":{"code_sha256":"59848503040bbdac"}},{"arxiv_id":"2203.07615","paper":"/paper/learning-what-not-to-segment-a-new","title":"Learning What Not to Segment: A New Perspective on Few-Shot Segmentation","date":"2022-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chunbolang/BAM","path":"util/dataset.py","file_url":"https://github.com/chunbolang/BAM/blob/HEAD/util/dataset.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0684a938c70bf564","mcp_get_code":{"code_sha256":"0684a938c70bf564"}},{"arxiv_id":"2201.12179","paper":"/paper/plug-play-attacks-towards-robust-and-flexible","title":"Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks","date":"2022-01-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ajbrock/BigGAN-PyTorch","path":"datasets.py","file_url":"https://github.com/ajbrock/BigGAN-PyTorch/blob/HEAD/datasets.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4e6a8faaf8e44abe","mcp_get_code":{"code_sha256":"4e6a8faaf8e44abe"}},{"arxiv_id":"2201.02279","paper":"/paper/de-rendering-3d-objects-in-the-wild","title":"De-rendering 3D Objects in the Wild","date":"2022-01-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"brummi/derender3d","path":"derender3d/dataloaders.py","file_url":"https://github.com/brummi/derender3d/blob/HEAD/derender3d/dataloaders.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"521cedf170be4833","mcp_get_code":{"code_sha256":"521cedf170be4833"}},{"arxiv_id":"2109.13228","paper":"/paper/pass-an-imagenet-replacement-for-self","title":"PASS: An ImageNet replacement for self-supervised pretraining without humans","date":"2021-09-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/dino","path":"eval_copy_detection.py","file_url":"https://github.com/facebookresearch/dino/blob/HEAD/eval_copy_detection.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"dfebfd56865b6379","mcp_get_code":{"code_sha256":"dfebfd56865b6379"}},{"arxiv_id":"2109.02288","paper":"/paper/toward-realistic-single-view-3d-object","title":"Toward Realistic Single-View 3D Object Reconstruction with Unsupervised Learning from Multiple Images","date":"2021-09-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vinairesearch/lemul","path":"dataloaders.py","file_url":"https://github.com/vinairesearch/lemul/blob/HEAD/dataloaders.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"521cedf170be4833","mcp_get_code":{"code_sha256":"521cedf170be4833"}},{"arxiv_id":"2108.09760","paper":"/paper/image-inpainting-via-conditional-texture-and","title":"Image Inpainting via Conditional Texture and Structure Dual Generation","date":"2021-08-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xiefan-guo/ctsdg","path":"datasets/folder.py","file_url":"https://github.com/xiefan-guo/ctsdg/blob/HEAD/datasets/folder.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"eb656fe08a6a70c4","mcp_get_code":{"code_sha256":"eb656fe08a6a70c4"}},{"arxiv_id":"2108.05293","paper":"/paper/few-shot-segmentation-with-global-and-local","title":"Few-Shot Segmentation with Global and Local Contrastive Learning","date":"2021-08-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liuweide01/GQNet-Few-shot-segmentation","path":"util/dataset.py","file_url":"https://github.com/liuweide01/GQNet-Few-shot-segmentation/blob/HEAD/util/dataset.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0684a938c70bf564","mcp_get_code":{"code_sha256":"0684a938c70bf564"}},{"arxiv_id":"2106.09681","paper":"/paper/xcit-cross-covariance-image-transformers","title":"XCiT: Cross-Covariance Image Transformers","date":"2021-06-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"adrienangeli/dino","path":"eval_copy_detection.py","file_url":"https://github.com/adrienangeli/dino/blob/HEAD/eval_copy_detection.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"dfebfd56865b6379","mcp_get_code":{"code_sha256":"dfebfd56865b6379"}},{"arxiv_id":"2105.14148","paper":"/paper/openmatch-open-set-consistency-regularization","title":"OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers","date":"2021-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VisionLearningGroup/OP_Match","path":"dataset/mydataset.py","file_url":"https://github.com/VisionLearningGroup/OP_Match/blob/HEAD/dataset/mydataset.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ab4109634b75ef8b","mcp_get_code":{"code_sha256":"ab4109634b75ef8b"}},{"arxiv_id":"2105.11527","paper":"/paper/unsupervised-visual-representation-learning-3","title":"Unsupervised Visual Representation Learning by Online Constrained K-Means","date":"2021-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"idstcv/coke","path":"coke/folder.py","file_url":"https://github.com/idstcv/coke/blob/HEAD/coke/folder.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"10b3abf638e3b68c","mcp_get_code":{"code_sha256":"10b3abf638e3b68c"}},{"arxiv_id":"2104.11493","paper":"/paper/stroke-based-scene-text-erasing-using","title":"Stroke-Based Scene Text Erasing Using Synthetic Data for Training","date":"2021-04-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tzm-tora/Stroke-Based-Scene-Text-Erasing","path":"src/dataset.py","file_url":"https://github.com/tzm-tora/Stroke-Based-Scene-Text-Erasing/blob/HEAD/src/dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0516d5020cce56da","mcp_get_code":{"code_sha256":"0516d5020cce56da"}},{"arxiv_id":"2104.10338","paper":"/paper/shadow-generation-for-composite-image-in-real","title":"Shadow Generation for Composite Image in Real-world Scenes","date":"2021-04-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bcmi/Object-Shadow-Generation-Dataset-DESOBA","path":"data_processing/soba_training_pairs.py","file_url":"https://github.com/bcmi/Object-Shadow-Generation-Dataset-DESOBA/blob/HEAD/data_processing/soba_training_pairs.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2383223cfe77b76c","mcp_get_code":{"code_sha256":"2383223cfe77b76c"}},{"arxiv_id":"2104.04314","paper":"/paper/cfnet-cascade-and-fused-cost-volume-for","title":"CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching","date":"2021-04-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gallenszl/MSMD-Net","path":"datasets/MiddleburyLoader.py","file_url":"https://github.com/gallenszl/MSMD-Net/blob/HEAD/datasets/MiddleburyLoader.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ab4109634b75ef8b","mcp_get_code":{"code_sha256":"ab4109634b75ef8b"}},{"arxiv_id":"2104.03954","paper":"/paper/de-rendering-the-world-s-revolutionary","title":"De-rendering the World's Revolutionary Artefacts","date":"2021-04-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"elliottwu/sorderender","path":"derender/dataloaders.py","file_url":"https://github.com/elliottwu/sorderender/blob/HEAD/derender/dataloaders.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"521cedf170be4833","mcp_get_code":{"code_sha256":"521cedf170be4833"}},{"arxiv_id":"2104.03344","paper":"/paper/ovanet-one-vs-all-network-for-universal","title":"OVANet: One-vs-All Network for Universal Domain Adaptation","date":"2021-04-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VisionLearningGroup/OVANet","path":"data_loader/mydataset.py","file_url":"https://github.com/VisionLearningGroup/OVANet/blob/HEAD/data_loader/mydataset.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ab4109634b75ef8b","mcp_get_code":{"code_sha256":"ab4109634b75ef8b"}},{"arxiv_id":"2103.14006","paper":"/paper/designing-a-practical-degradation-model-for","title":"Designing a Practical Degradation Model for Deep Blind Image Super-Resolution","date":"2021-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kadirnar/bsrgan-pip","path":"bsrgan/utils/utils_image.py","file_url":"https://github.com/kadirnar/bsrgan-pip/blob/HEAD/bsrgan/utils/utils_image.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"226f6afcd57ff476","mcp_get_code":{"code_sha256":"226f6afcd57ff476"}},{"arxiv_id":"2009.11551","paper":"/paper/residual-feature-distillation-network-for","title":"Residual Feature Distillation Network for Lightweight Image Super-Resolution","date":"2020-09-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"njulj/RFDN","path":"utils/utils_image.py","file_url":"https://github.com/njulj/RFDN/blob/HEAD/utils/utils_image.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ab4109634b75ef8b","mcp_get_code":{"code_sha256":"ab4109634b75ef8b"}},{"arxiv_id":"2008.10599","paper":"/paper/the-hessian-penalty-a-weak-prior-for","title":"The Hessian Penalty: A Weak Prior for Unsupervised Disentanglement","date":"2020-08-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wpeebles/hessian_penalty","path":"biggan_discovery/datasets.py","file_url":"https://github.com/wpeebles/hessian_penalty/blob/HEAD/biggan_discovery/datasets.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"4e6a8faaf8e44abe","mcp_get_code":{"code_sha256":"4e6a8faaf8e44abe"}},{"arxiv_id":"2008.09188","paper":"/paper/detecting-natural-disasters-damage-and","title":"Detecting natural disasters, damage, and incidents in the wild","date":"2020-08-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ethanweber/IncidentsDataset","path":"dataset.py","file_url":"https://github.com/ethanweber/IncidentsDataset/blob/HEAD/dataset.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4e6a8faaf8e44abe","mcp_get_code":{"code_sha256":"4e6a8faaf8e44abe"}},{"arxiv_id":"2007.04250","paper":"/paper/a-benchmark-of-medical-out-of-distribution","title":"A Benchmark of Medical Out of Distribution Detection","date":"2020-07-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"caotians1/OD-test-master","path":"datasets/TinyImagenet.py","file_url":"https://github.com/caotians1/OD-test-master/blob/HEAD/datasets/TinyImagenet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4e6a8faaf8e44abe","mcp_get_code":{"code_sha256":"4e6a8faaf8e44abe"}},{"arxiv_id":"2007.03085","paper":"/paper/wasserstein-distances-for-stereo-disparity","title":"Wasserstein Distances for Stereo Disparity Estimation","date":"2020-07-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Div99/W-Stereo-Disp","path":"src/disp_dataloader/KITTILoader3D.py","file_url":"https://github.com/Div99/W-Stereo-Disp/blob/HEAD/src/disp_dataloader/KITTILoader3D.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ab4109634b75ef8b","mcp_get_code":{"code_sha256":"ab4109634b75ef8b"}},{"arxiv_id":"2005.10954","paper":"/paper/head2head-video-based-neural-head-synthesis","title":"Head2Head: Video-based Neural Head Synthesis","date":"2020-05-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"michaildoukas/head2head","path":"preprocessing/compute_distances.py","file_url":"https://github.com/michaildoukas/head2head/blob/HEAD/preprocessing/compute_distances.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"70425fe6d8c34c2c","mcp_get_code":{"code_sha256":"70425fe6d8c34c2c"}},{"arxiv_id":"2004.10016","paper":"/paper/unsupervised-domain-adaptation-through-inter","title":"Unsupervised Domain Adaptation through Inter-modal Rotation for RGB-D Object Recognition","date":"2020-04-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MRLoghmani/relative-rotation","path":"code/data_loader.py","file_url":"https://github.com/MRLoghmani/relative-rotation/blob/HEAD/code/data_loader.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ab4109634b75ef8b","mcp_get_code":{"code_sha256":"ab4109634b75ef8b"}},{"arxiv_id":"2004.09484","paper":"/paper/bringing-old-photos-back-to-life","title":"Bringing Old Photos Back to Life","date":"2020-04-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yasamin-niknam/Image-Restoration","path":"Face_Enhancement/data_FE/image_folder.py","file_url":"https://github.com/yasamin-niknam/Image-Restoration/blob/HEAD/Face_Enhancement/data_FE/image_folder.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"e559cb5e8a6388f6","mcp_get_code":{"code_sha256":"e559cb5e8a6388f6"}},{"arxiv_id":"2004.09199","paper":"/paper/generative-feature-replay-for-class","title":"Generative Feature Replay For Class-Incremental Learning","date":"2020-04-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xialeiliu/GFR-IL","path":"ImageFolder.py","file_url":"https://github.com/xialeiliu/GFR-IL/blob/HEAD/ImageFolder.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f5510162ccebc03a","mcp_get_code":{"code_sha256":"f5510162ccebc03a"}},{"arxiv_id":"2004.00917","paper":"/paper/controllable-orthogonalization-in-training","title":"Controllable Orthogonalization in Training DNNs","date":"2020-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huangleiBuaa/ONI","path":"ONI_PyTorch/GAN/datasets.py","file_url":"https://github.com/huangleiBuaa/ONI/blob/HEAD/ONI_PyTorch/GAN/datasets.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"4e6a8faaf8e44abe","mcp_get_code":{"code_sha256":"4e6a8faaf8e44abe"}},{"arxiv_id":"2003.14297","paper":"/paper/learning-from-small-data-through-sampling-an","title":"Generative Latent Implicit Conditional Optimization when Learning from Small Sample","date":"2020-03-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IdanAzuri/glico-learning-small-sample","path":"glico_model/imagent_folder_loader.py","file_url":"https://github.com/IdanAzuri/glico-learning-small-sample/blob/HEAD/glico_model/imagent_folder_loader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3ed19559ece460a6","mcp_get_code":{"code_sha256":"3ed19559ece460a6"}},{"arxiv_id":"2003.13328","paper":"/paper/2003-13328","title":"Strip Pooling: Rethinking Spatial Pooling for Scene Parsing","date":"2020-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Andrew-Qibin/SPNet","path":"util/dataset.py","file_url":"https://github.com/Andrew-Qibin/SPNet/blob/HEAD/util/dataset.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0684a938c70bf564","mcp_get_code":{"code_sha256":"0684a938c70bf564"}},{"arxiv_id":"2003.13170","paper":"/paper/space-time-aware-multi-resolution-video","title":"Space-Time-Aware Multi-Resolution Video Enhancement","date":"2020-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alterzero/STARnet","path":"dataset.py","file_url":"https://github.com/alterzero/STARnet/blob/HEAD/dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0516d5020cce56da","mcp_get_code":{"code_sha256":"0516d5020cce56da"}},{"arxiv_id":"2003.12267","paper":"/paper/controllable-person-image-synthesis-with","title":"Controllable Person Image Synthesis with Attribute-Decomposed GAN","date":"2020-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sgoldyaev/DeepFashion.ADGAN","path":"tool/generate_fashion_datasets.py","file_url":"https://github.com/sgoldyaev/DeepFashion.ADGAN/blob/HEAD/tool/generate_fashion_datasets.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ab4109634b75ef8b","mcp_get_code":{"code_sha256":"ab4109634b75ef8b"}},{"arxiv_id":"2003.08607","paper":"/paper/unsupervised-domain-adaptation-via-4","title":"Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering","date":"2020-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gorilla-lab-scut/srdc-cvpr2020","path":"utils/folder.py","file_url":"https://github.com/gorilla-lab-scut/srdc-cvpr2020/blob/HEAD/utils/folder.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2d2234ff68f9f460","mcp_get_code":{"code_sha256":"2d2234ff68f9f460"}},{"arxiv_id":"2003.08436","paper":"/paper/collaborative-distillation-for-ultra","title":"Collaborative Distillation for Ultra-Resolution Universal Style Transfer","date":"2020-03-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mingsun-tse/collaborative-distillation","path":"PytorchWCT/data_loader.py","file_url":"https://github.com/mingsun-tse/collaborative-distillation/blob/HEAD/PytorchWCT/data_loader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0516d5020cce56da","mcp_get_code":{"code_sha256":"0516d5020cce56da"}},{"arxiv_id":"2001.03994","paper":"/paper/fast-is-better-than-free-revisiting-1","title":"Fast is better than free: Revisiting adversarial training","date":"2020-01-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aliborji/Shapedefence","path":"make_imagenet_64_c.py","file_url":"https://github.com/aliborji/Shapedefence/blob/HEAD/make_imagenet_64_c.py","status":"ran_violates","verification_level":2,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"89c2cf3b97685684","mcp_get_code":{"code_sha256":"89c2cf3b97685684"}},{"arxiv_id":"1912.06704","paper":"/paper/hierarchical-deep-stereo-matching-on-high-1","title":"Hierarchical Deep Stereo Matching on High-resolution Images","date":"2019-12-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gengshan-y/high-res-stereo","path":"dataloader/KITTIloader2012.py","file_url":"https://github.com/gengshan-y/high-res-stereo/blob/HEAD/dataloader/KITTIloader2012.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ab4109634b75ef8b","mcp_get_code":{"code_sha256":"ab4109634b75ef8b"}},{"arxiv_id":"1912.05270","paper":"/paper/minegan-effective-knowledge-transfer-from","title":"MineGAN: effective knowledge transfer from GANs to target domains with few images","date":"2019-12-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yaxingwang/MineGAN","path":"datasets.py","file_url":"https://github.com/yaxingwang/MineGAN/blob/HEAD/datasets.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4e6a8faaf8e44abe","mcp_get_code":{"code_sha256":"4e6a8faaf8e44abe"}},{"arxiv_id":"1911.11130","paper":"/paper/unsupervised-learning-of-probably-symmetric","title":"Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild","date":"2019-11-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"elliottwu/unsup3d","path":"unsup3d/dataloaders.py","file_url":"https://github.com/elliottwu/unsup3d/blob/HEAD/unsup3d/dataloaders.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"521cedf170be4833","mcp_get_code":{"code_sha256":"521cedf170be4833"}},{"arxiv_id":"1911.07850","paper":"/paper/frequency-separation-for-real-world-super","title":"Frequency Separation for Real-World Super-Resolution","date":"2019-11-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ManuelFritsche/real-world-sr","path":"dsgan/utils.py","file_url":"https://github.com/ManuelFritsche/real-world-sr/blob/HEAD/dsgan/utils.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2383223cfe77b76c","mcp_get_code":{"code_sha256":"2383223cfe77b76c"}},{"arxiv_id":"1911.07205","paper":"/paper/refit-a-unified-watermark-removal-framework","title":"REFIT: A Unified Watermark Removal Framework For Deep Learning Systems With Limited Data","date":"2019-11-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sunblaze-ucb/REFIT","path":"helpers/ImageFolderCustomClass.py","file_url":"https://github.com/sunblaze-ucb/REFIT/blob/HEAD/helpers/ImageFolderCustomClass.py","status":"ran_violates","verification_level":2,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"89c2cf3b97685684","mcp_get_code":{"code_sha256":"89c2cf3b97685684"}},{"arxiv_id":"1909.07425","paper":"/paper/a-characteristic-function-approach-to-deep","title":"A Characteristic Function Approach to Deep Implicit Generative Modeling","date":"2019-09-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"crslab/OCFGAN","path":"src/datasets.py","file_url":"https://github.com/crslab/OCFGAN/blob/HEAD/src/datasets.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5fff86ff937930aa","mcp_get_code":{"code_sha256":"5fff86ff937930aa"}},{"arxiv_id":"1908.01311","paper":"/paper/fully-automatic-video-colorization-with-self-1","title":"Fully Automatic Video Colorization with Self-Regularization and Diversity","date":"2019-08-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ChenyangLEI/Fully-Automatic-Video-Colorization-with-Self-Regularization-and-Diversity","path":"utils.py","file_url":"https://github.com/ChenyangLEI/Fully-Automatic-Video-Colorization-with-Self-Regularization-and-Diversity/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"25d498f122b53d79","mcp_get_code":{"code_sha256":"25d498f122b53d79"}},{"arxiv_id":"1906.11129","paper":"/paper/uncertainty-guided-multi-scale-residual-1","title":"Uncertainty Guided Multi-Scale Residual Learning-using a Cycle Spinning CNN for Single Image De-Raining","date":"2019-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rajeevyasarla/UMRL--using-Cycle-Spinning","path":"datasets/classification.py","file_url":"https://github.com/rajeevyasarla/UMRL--using-Cycle-Spinning/blob/HEAD/datasets/classification.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ab4109634b75ef8b","mcp_get_code":{"code_sha256":"ab4109634b75ef8b"}},{"arxiv_id":"1906.11129","paper":"/paper/uncertainty-guided-multi-scale-residual-1","title":"Uncertainty Guided Multi-Scale Residual Learning-using a Cycle Spinning CNN for Single Image De-Raining","date":"2019-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rajeevyasarla/UMRL--using-Cycle-Spinning","path":"datasets/pix2pix.py","file_url":"https://github.com/rajeevyasarla/UMRL--using-Cycle-Spinning/blob/HEAD/datasets/pix2pix.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9c8620ddd189a741","mcp_get_code":{"code_sha256":"9c8620ddd189a741"}},{"arxiv_id":"1906.06310","paper":"/paper/pseudo-lidar-accurate-depth-for-3d-object","title":"Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving","date":"2019-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mileyan/Pseudo_Lidar_V2","path":"src/dataloader/KITTILoader3D.py","file_url":"https://github.com/mileyan/Pseudo_Lidar_V2/blob/HEAD/src/dataloader/KITTILoader3D.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ab4109634b75ef8b","mcp_get_code":{"code_sha256":"ab4109634b75ef8b"}},{"arxiv_id":"1905.11946","paper":"/paper/efficientnet-rethinking-model-scaling-for","title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","date":"2019-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"amirdy/dog-breed-classification","path":"Web_App/files/folder.py","file_url":"https://github.com/amirdy/dog-breed-classification/blob/HEAD/Web_App/files/folder.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"54d4745a566d733c","mcp_get_code":{"code_sha256":"54d4745a566d733c"}},{"arxiv_id":"1905.06368","paper":"/paper/190506368","title":"Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-High Resolution Images","date":"2019-05-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenwydj/ultra_high_resolution_segmentation","path":"dataset/deep_globe.py","file_url":"https://github.com/chenwydj/ultra_high_resolution_segmentation/blob/HEAD/dataset/deep_globe.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0516d5020cce56da","mcp_get_code":{"code_sha256":"0516d5020cce56da"}},{"arxiv_id":"1904.04717","paper":"/paper/label-propagation-for-deep-semi-supervised","title":"Label Propagation for Deep Semi-supervised Learning","date":"2019-04-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ahmetius/LP-DeepSSL","path":"lp/db_semisuper.py","file_url":"https://github.com/ahmetius/LP-DeepSSL/blob/HEAD/lp/db_semisuper.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f5510162ccebc03a","mcp_get_code":{"code_sha256":"f5510162ccebc03a"}},{"arxiv_id":"1903.10128","paper":"/paper/recurrent-back-projection-network-for-video","title":"Recurrent Back-Projection Network for Video Super-Resolution","date":"2019-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alterzero/RBPN-PyTorch","path":"dataset.py","file_url":"https://github.com/alterzero/RBPN-PyTorch/blob/HEAD/dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0516d5020cce56da","mcp_get_code":{"code_sha256":"0516d5020cce56da"}},{"arxiv_id":"1903.10128","paper":"/paper/recurrent-back-projection-network-for-video","title":"Recurrent Back-Projection Network for Video Super-Resolution","date":"2019-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhangtianmingxp/rbpn_mindspore","path":"src/datasets/dataset.py","file_url":"https://github.com/zhangtianmingxp/rbpn_mindspore/blob/HEAD/src/datasets/dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"a20de656266e3549","mcp_get_code":{"code_sha256":"a20de656266e3549"}},{"arxiv_id":"1903.09760","paper":"/paper/photorealistic-style-transfer-via-wavelet","title":"Photorealistic Style Transfer via Wavelet Transforms","date":"2019-03-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"59848503040bbdac","mcp_get_code":{"code_sha256":"59848503040bbdac"}},{"arxiv_id":"1902.02476","paper":"/paper/a-simple-baseline-for-bayesian-uncertainty-in","title":"A Simple Baseline for Bayesian Uncertainty in Deep Learning","date":"2019-02-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wjmaddox/swa_gaussian","path":"swag/camvid.py","file_url":"https://github.com/wjmaddox/swa_gaussian/blob/HEAD/swag/camvid.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"af447e65a04b459e","mcp_get_code":{"code_sha256":"af447e65a04b459e"}},{"arxiv_id":"1901.04111","paper":"/paper/fast-and-robust-multi-person-3d-pose","title":"Fast and Robust Multi-Person 3D Pose Estimation from Multiple Views","date":"2019-01-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"perfanalytics/pose2sim","path":"Pose2Sim/common.py","file_url":"https://github.com/perfanalytics/pose2sim/blob/HEAD/Pose2Sim/common.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"728c82d2ed4d8df5","mcp_get_code":{"code_sha256":"728c82d2ed4d8df5"}},{"arxiv_id":"1812.10366","paper":"/paper/a-poisson-gaussian-denoising-dataset-with","title":"A Poisson-Gaussian Denoising Dataset with Real Fluorescence Microscopy Images","date":"2018-12-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bmmi/denoising-fluorescence","path":"denoising/utils/data_loader.py","file_url":"https://github.com/bmmi/denoising-fluorescence/blob/HEAD/denoising/utils/data_loader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3002f15097305815","mcp_get_code":{"code_sha256":"3002f15097305815"}},{"arxiv_id":"1810.11408","paper":"/paper/anytime-stereo-image-depth-estimation-on","title":"Anytime Stereo Image Depth Estimation on Mobile Devices","date":"2018-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rajeevpatwari/anynet","path":"dataloader/diy_dataset.py","file_url":"https://github.com/rajeevpatwari/anynet/blob/HEAD/dataloader/diy_dataset.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"59848503040bbdac","mcp_get_code":{"code_sha256":"59848503040bbdac"}},{"arxiv_id":"1810.11408","paper":"/paper/anytime-stereo-image-depth-estimation-on","title":"Anytime Stereo Image Depth Estimation on Mobile Devices","date":"2018-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mileyan/AnyNet","path":"dataloader/KITTILoader.py","file_url":"https://github.com/mileyan/AnyNet/blob/HEAD/dataloader/KITTILoader.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ab4109634b75ef8b","mcp_get_code":{"code_sha256":"ab4109634b75ef8b"}},{"arxiv_id":"1810.11408","paper":"/paper/anytime-stereo-image-depth-estimation-on","title":"Anytime Stereo Image Depth Estimation on Mobile Devices","date":"2018-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mamoanwar97/Anynet_modified","path":"dataloader/KITTI_dataset.py","file_url":"https://github.com/mamoanwar97/Anynet_modified/blob/HEAD/dataloader/KITTI_dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"44b43878a62b8e71","mcp_get_code":{"code_sha256":"44b43878a62b8e71"}},{"arxiv_id":"1809.11096","paper":"/paper/large-scale-gan-training-for-high-fidelity","title":"Large Scale GAN Training for High Fidelity Natural Image Synthesis","date":"2018-09-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kidist-amde/biggan-pytorch","path":"datasets.py","file_url":"https://github.com/kidist-amde/biggan-pytorch/blob/HEAD/datasets.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4e6a8faaf8e44abe","mcp_get_code":{"code_sha256":"4e6a8faaf8e44abe"}},{"arxiv_id":"1809.04729","paper":"/paper/does-your-model-know-the-digit-6-is-not-a-cat","title":"A Less Biased Evaluation of Out-of-distribution Sample Detectors","date":"2018-09-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"safednn-group/nap-ood","path":"datasets/TinyImagenet.py","file_url":"https://github.com/safednn-group/nap-ood/blob/HEAD/datasets/TinyImagenet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4e6a8faaf8e44abe","mcp_get_code":{"code_sha256":"4e6a8faaf8e44abe"}},{"arxiv_id":"1805.08318","paper":"/paper/self-attention-generative-adversarial","title":"Self-Attention Generative Adversarial Networks","date":"2018-05-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Atmosphere-art/Self-Attention-GAN","path":"dataset.py","file_url":"https://github.com/Atmosphere-art/Self-Attention-GAN/blob/HEAD/dataset.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4e6a8faaf8e44abe","mcp_get_code":{"code_sha256":"4e6a8faaf8e44abe"}},{"arxiv_id":"1803.08669","paper":"/paper/pyramid-stereo-matching-network","title":"Pyramid Stereo Matching Network","date":"2018-03-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JiaRenChang/PSMNet","path":"dataloader/KITTILoader.py","file_url":"https://github.com/JiaRenChang/PSMNet/blob/HEAD/dataloader/KITTILoader.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ab4109634b75ef8b","mcp_get_code":{"code_sha256":"ab4109634b75ef8b"}},{"arxiv_id":"1803.02735","paper":"/paper/deep-back-projection-networks-for-super","title":"Deep Back-Projection Networks For Super-Resolution","date":"2018-03-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alterzero/DBPN-Pytorch","path":"dataset.py","file_url":"https://github.com/alterzero/DBPN-Pytorch/blob/HEAD/dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0516d5020cce56da","mcp_get_code":{"code_sha256":"0516d5020cce56da"}},{"arxiv_id":"1802.08797","paper":"/paper/residual-dense-network-for-image-super","title":"Residual Dense Network for Image Super-Resolution","date":"2018-02-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"puffnjackie/pytorch-super-resolution-implementations","path":"dataset.py","file_url":"https://github.com/puffnjackie/pytorch-super-resolution-implementations/blob/HEAD/dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"0516d5020cce56da","mcp_get_code":{"code_sha256":"0516d5020cce56da"}},{"arxiv_id":"1802.06474","paper":"/paper/a-closed-form-solution-to-photorealistic","title":"A Closed-form Solution to Photorealistic Image Stylization","date":"2018-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"59848503040bbdac","mcp_get_code":{"code_sha256":"59848503040bbdac"}},{"arxiv_id":"1710.10916","paper":"/paper/stackgan-realistic-image-synthesis-with","title":"StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks","date":"2017-10-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hanzhanggit/StackGAN-v2","path":"code/datasets.py","file_url":"https://github.com/hanzhanggit/StackGAN-v2/blob/HEAD/code/datasets.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ab4109634b75ef8b","mcp_get_code":{"code_sha256":"ab4109634b75ef8b"}},{"arxiv_id":"1703.07511","paper":"/paper/deep-photo-style-transfer","title":"Deep Photo Style Transfer","date":"2017-03-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"clovaai/WCT2","path":"transfer.py","file_url":"https://github.com/clovaai/WCT2/blob/HEAD/transfer.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"59848503040bbdac","mcp_get_code":{"code_sha256":"59848503040bbdac"}},{"arxiv_id":"1703.06868","paper":"/paper/arbitrary-style-transfer-in-real-time-with","title":"Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization","date":"2017-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"asindel/artfacepoints","path":"data_utils.py","file_url":"https://github.com/asindel/artfacepoints/blob/HEAD/data_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"eafb5322ecc71e47","mcp_get_code":{"code_sha256":"eafb5322ecc71e47"}},{"arxiv_id":"1611.07004","paper":"/paper/image-to-image-translation-with-conditional","title":"Image-to-Image Translation with Conditional Adversarial Networks","date":"2016-11-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"telecombcn-dl/2018-dlcv-team2","path":"util.py","file_url":"https://github.com/telecombcn-dl/2018-dlcv-team2/blob/HEAD/util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bd95cdc5e41ec8e8","mcp_get_code":{"code_sha256":"bd95cdc5e41ec8e8"}},{"arxiv_id":"1609.05158","paper":"/paper/real-time-single-image-and-video-super","title":"Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network","date":"2016-09-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"niazwazir/REAL_TIME_VIDEO_IMSR","path":"data_utils.py","file_url":"https://github.com/niazwazir/REAL_TIME_VIDEO_IMSR/blob/HEAD/data_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7e6cb8a85c64996e","mcp_get_code":{"code_sha256":"7e6cb8a85c64996e"}},{"arxiv_id":"1609.04802","paper":"/paper/photo-realistic-single-image-super-resolution","title":"Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network","date":"2016-09-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shirsenduhalder/SRResGAN-Improved-Perceptual","path":"dataset_helper/common.py","file_url":"https://github.com/shirsenduhalder/SRResGAN-Improved-Perceptual/blob/HEAD/dataset_helper/common.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ab4109634b75ef8b","mcp_get_code":{"code_sha256":"ab4109634b75ef8b"}},{"arxiv_id":"1609.04802","paper":"/paper/photo-realistic-single-image-super-resolution","title":"Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network","date":"2016-09-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"leftthomas/SRGAN","path":"data_utils.py","file_url":"https://github.com/leftthomas/SRGAN/blob/HEAD/data_utils.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2383223cfe77b76c","mcp_get_code":{"code_sha256":"2383223cfe77b76c"}},{"arxiv_id":"1603.08511","paper":"/paper/colorful-image-colorization","title":"Colorful Image Colorization","date":"2016-03-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Callifrey/Paddle-CIC","path":"dataset.py","file_url":"https://github.com/Callifrey/Paddle-CIC/blob/HEAD/dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"e12f5ee67cba16e9","mcp_get_code":{"code_sha256":"e12f5ee67cba16e9"}},{"arxiv_id":"1406.2661","paper":"/paper/generative-adversarial-networks","title":"Generative Adversarial Networks","date":"2014-06-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"WANG-Chaoyue/EvolutionaryGAN-pytorch","path":"inception_pytorch/datasets.py","file_url":"https://github.com/WANG-Chaoyue/EvolutionaryGAN-pytorch/blob/HEAD/inception_pytorch/datasets.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4e6a8faaf8e44abe","mcp_get_code":{"code_sha256":"4e6a8faaf8e44abe"}},{"arxiv_id":"aaai_28536","paper":null,"title":"arXiv:aaai_28536","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"HaoZhang1018/RMR-Fusion","path":"utils.py","file_url":"https://github.com/HaoZhang1018/RMR-Fusion/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"86889dc6e9a74a48","mcp_get_code":{"code_sha256":"86889dc6e9a74a48"}},{"arxiv_id":"aaai_28243","paper":null,"title":"arXiv:aaai_28243","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"tommy-xq/SA2VP","path":"dataset_folder.py","file_url":"https://github.com/tommy-xq/SA2VP/blob/HEAD/dataset_folder.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8ac50ae8773b9431","mcp_get_code":{"code_sha256":"8ac50ae8773b9431"}},{"arxiv_id":"aaai_28199","paper":null,"title":"arXiv:aaai_28199","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"YoferChen/FedST","path":"core/base_dataset.py","file_url":"https://github.com/YoferChen/FedST/blob/HEAD/core/base_dataset.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ab4109634b75ef8b","mcp_get_code":{"code_sha256":"ab4109634b75ef8b"}},{"arxiv_id":"aaai_25604","paper":null,"title":"arXiv:aaai_25604","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"xcyao00/PMAD","path":"datasets/mvtec_train.py","file_url":"https://github.com/xcyao00/PMAD/blob/HEAD/datasets/mvtec_train.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8ac50ae8773b9431","mcp_get_code":{"code_sha256":"8ac50ae8773b9431"}},{"arxiv_id":"aaai_20902","paper":null,"title":"arXiv:aaai_20902","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"bxz9200/CLPA","path":"datasets.py","file_url":"https://github.com/bxz9200/CLPA/blob/HEAD/datasets.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4e6a8faaf8e44abe","mcp_get_code":{"code_sha256":"4e6a8faaf8e44abe"}},{"arxiv_id":"aaai_20247","paper":null,"title":"arXiv:aaai_20247","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"csmiler/ProbeGAN","path":"datasets.py","file_url":"https://github.com/csmiler/ProbeGAN/blob/HEAD/datasets.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4e6a8faaf8e44abe","mcp_get_code":{"code_sha256":"4e6a8faaf8e44abe"}},{"arxiv_id":"aaai_20056","paper":null,"title":"arXiv:aaai_20056","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"SpadeLiu/Lac-GwcNet","path":"dataloader/KITTI2012loader.py","file_url":"https://github.com/SpadeLiu/Lac-GwcNet/blob/HEAD/dataloader/KITTI2012loader.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ab4109634b75ef8b","mcp_get_code":{"code_sha256":"ab4109634b75ef8b"}},{"arxiv_id":"Zhou_Adapt_or_Perish_Adaptive_Sparse_Transformer_with_Attentive_Feature_Refinement_CVPR_2024_paper","paper":null,"title":"arXiv:Zhou_Adapt_or_Perish_Adaptive_Sparse_Transformer_with_Attentive_Feature_Refinement_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"joshyZhou/AST","path":"dataset/dataset_dehaze_denseHaze.py","file_url":"https://github.com/joshyZhou/AST/blob/HEAD/dataset/dataset_dehaze_denseHaze.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"91fca7a2b44569ad","mcp_get_code":{"code_sha256":"91fca7a2b44569ad"}},{"arxiv_id":"Zhang_Learning_Rain_Location_Prior_for_Nighttime_Deraining_ICCV_2023_paper","paper":null,"title":"arXiv:Zhang_Learning_Rain_Location_Prior_for_Nighttime_Deraining_ICCV_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"zkawfanx/RLP","path":"rlp/dataset.py","file_url":"https://github.com/zkawfanx/RLP/blob/HEAD/rlp/dataset.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"91fca7a2b44569ad","mcp_get_code":{"code_sha256":"91fca7a2b44569ad"}},{"arxiv_id":"Ren_Masked_Jigsaw_Puzzle_A_Versatile_Position_Embedding_for_Vision_Transformers_CVPR_2023_paper","paper":null,"title":"arXiv:Ren_Masked_Jigsaw_Puzzle_A_Versatile_Position_Embedding_for_Vision_Transformers_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"yhlleo/MJP","path":"eval/prepare_imagenet_c.py","file_url":"https://github.com/yhlleo/MJP/blob/HEAD/eval/prepare_imagenet_c.py","status":"ran_violates","verification_level":2,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"89c2cf3b97685684","mcp_get_code":{"code_sha256":"89c2cf3b97685684"}},{"arxiv_id":"Ren_Adaptive_Consistency_Prior_Based_Deep_Network_for_Image_Denoising_CVPR_2021_paper","paper":null,"title":"arXiv:Ren_Adaptive_Consistency_Prior_Based_Deep_Network_for_Image_Denoising_CVPR_2021_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"chaoren88/DeamNet","path":"dataset.py","file_url":"https://github.com/chaoren88/DeamNet/blob/HEAD/dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0516d5020cce56da","mcp_get_code":{"code_sha256":"0516d5020cce56da"}},{"arxiv_id":"Park_LANIT_Language-Driven_Image-to-Image_Translation_for_Unlabeled_Data_CVPR_2023_paper","paper":null,"title":"arXiv:Park_LANIT_Language-Driven_Image-to-Image_Translation_for_Unlabeled_Data_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"KU-CVLAB/LANIT","path":"core/custom_dataset.py","file_url":"https://github.com/KU-CVLAB/LANIT/blob/HEAD/core/custom_dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"10b3abf638e3b68c","mcp_get_code":{"code_sha256":"10b3abf638e3b68c"}},{"arxiv_id":"Li_From_Contexts_to_Locality_Ultra-High_Resolution_Image_Segmentation_via_Locality-Aware_ICCV_2021_paper","paper":null,"title":"arXiv:Li_From_Contexts_to_Locality_Ultra-High_Resolution_Image_Segmentation_via_Locality-Aware_ICCV_2021_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"liqiokkk/FCtL","path":"dataset/deep_globe.py","file_url":"https://github.com/liqiokkk/FCtL/blob/HEAD/dataset/deep_globe.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a1b94aeca81e7bfd","mcp_get_code":{"code_sha256":"a1b94aeca81e7bfd"}},{"arxiv_id":"Hajimiri_A_Strong_Baseline_for_Generalized_Few-Shot_Semantic_Segmentation_CVPR_2023_paper","paper":null,"title":"arXiv:Hajimiri_A_Strong_Baseline_for_Generalized_Few-Shot_Semantic_Segmentation_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"sinahmr/DIaM","path":"src/dataset/utils.py","file_url":"https://github.com/sinahmr/DIaM/blob/HEAD/src/dataset/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"046826238a5f7d1f","mcp_get_code":{"code_sha256":"046826238a5f7d1f"}},{"arxiv_id":"136780474","paper":null,"title":"arXiv:136780474","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"dengyecode/hourglassattention","path":"dataloader/image_folder.py","file_url":"https://github.com/dengyecode/hourglassattention/blob/HEAD/dataloader/image_folder.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ab4109634b75ef8b","mcp_get_code":{"code_sha256":"ab4109634b75ef8b"}}]}