{"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/check-integrity","entry":"check_integrity","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":37,"n_papers_ran":17,"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":9,"n_samples_ran":4,"n_samples_fingerprinted":4,"n_places":37,"n_places_pointer_only":13,"by_status":{"ran_honours":0,"ran_violates":3,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"unverified":5},"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":"2412.05926","paper":"/paper/bidm-pushing-the-limit-of-quantization-for","title":"BiDM: Pushing the Limit of Quantization for Diffusion Models","date":"2024-12-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xingyu-zheng/bidm","path":"bidm-cifar/datasets/utils.py","file_url":"https://github.com/xingyu-zheng/bidm/blob/HEAD/bidm-cifar/datasets/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7345763c9b9300fb","mcp_get_code":{"code_sha256":"7345763c9b9300fb"}},{"arxiv_id":"2409.10956","paper":"/paper/versatile-incremental-learning-towards-class","title":"Versatile Incremental Learning: Towards Class and Domain-Agnostic Incremental Learning","date":"2024-09-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"khu-agi/vil","path":"continual_datasets/dataset_utils.py","file_url":"https://github.com/khu-agi/vil/blob/HEAD/continual_datasets/dataset_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"46b62ccc9e5b9878","mcp_get_code":{"code_sha256":"46b62ccc9e5b9878"}},{"arxiv_id":"2407.09896","paper":"/paper/zero-shot-image-compression-with-diffusion","title":"PSC: Posterior Sampling-Based Compression","date":"2024-07-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"noamelata/AdaSense","path":"datasets/utils.py","file_url":"https://github.com/noamelata/AdaSense/blob/HEAD/datasets/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7345763c9b9300fb","mcp_get_code":{"code_sha256":"7345763c9b9300fb"}},{"arxiv_id":"2403.07591","paper":"/paper/robustifying-and-boosting-training-free","title":"Robustifying and Boosting Training-Free Neural Architecture Search","date":"2024-03-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hzf1174/RoBoT","path":"foresight/imagenet16.py","file_url":"https://github.com/hzf1174/RoBoT/blob/HEAD/foresight/imagenet16.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"e947a6effcaaf32b","mcp_get_code":{"code_sha256":"e947a6effcaaf32b"}},{"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/dataset_utils.py","file_url":"https://github.com/ermongroup/fairgen/blob/HEAD/src/KL-BigGAN/dataset_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7345763c9b9300fb","mcp_get_code":{"code_sha256":"7345763c9b9300fb"}},{"arxiv_id":"2402.15152","paper":"/paper/on-the-duality-between-sharpness-aware","title":"On the Duality Between Sharpness-Aware Minimization and Adversarial Training","date":"2024-02-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"weizeming/SAM_AT","path":"SAM_segmentation/datasets/utils.py","file_url":"https://github.com/weizeming/SAM_AT/blob/HEAD/SAM_segmentation/datasets/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7345763c9b9300fb","mcp_get_code":{"code_sha256":"7345763c9b9300fb"}},{"arxiv_id":"2312.16519","paper":"/paper/image-restoration-by-denoising-diffusion","title":"Image Restoration by Denoising Diffusion Models with Iteratively Preconditioned Guidance","date":"2023-12-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tirer-lab/ddpg","path":"datasets/utils.py","file_url":"https://github.com/tirer-lab/ddpg/blob/HEAD/datasets/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7345763c9b9300fb","mcp_get_code":{"code_sha256":"7345763c9b9300fb"}},{"arxiv_id":"2312.12030","paper":"/paper/towards-accurate-guided-diffusion-sampling","title":"Towards Accurate Guided Diffusion Sampling through Symplectic Adjoint Method","date":"2023-12-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hanshuyan/adjointdpm","path":"ddpm_and_guided-diffusion/datasets/utils.py","file_url":"https://github.com/hanshuyan/adjointdpm/blob/HEAD/ddpm_and_guided-diffusion/datasets/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7345763c9b9300fb","mcp_get_code":{"code_sha256":"7345763c9b9300fb"}},{"arxiv_id":"2311.01226","paper":"/paper/optimal-transport-guided-conditional-score","title":"Optimal Transport-Guided Conditional Score-Based Diffusion Models","date":"2023-11-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"XJTU-XGU/OTCS","path":"datasets/utils.py","file_url":"https://github.com/XJTU-XGU/OTCS/blob/HEAD/datasets/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7345763c9b9300fb","mcp_get_code":{"code_sha256":"7345763c9b9300fb"}},{"arxiv_id":"2310.06368","paper":"/paper/coinseg-contrast-inter-and-intra-class-1","title":"CoinSeg: Contrast Inter- and Intra- Class Representations for Incremental Segmentation","date":"2023-10-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zkzhang98/coinseg","path":"datasets/utils.py","file_url":"https://github.com/zkzhang98/coinseg/blob/HEAD/datasets/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7345763c9b9300fb","mcp_get_code":{"code_sha256":"7345763c9b9300fb"}},{"arxiv_id":"2308.09610","paper":"/paper/on-the-effectiveness-of-layernorm-tuning-for","title":"On the Effectiveness of LayerNorm Tuning for Continual Learning in Vision Transformers","date":"2023-08-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tdemin16/continual-layernorm-tuning","path":"couple/continual_datasets/dataset_utils.py","file_url":"https://github.com/tdemin16/continual-layernorm-tuning/blob/HEAD/couple/continual_datasets/dataset_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"46b62ccc9e5b9878","mcp_get_code":{"code_sha256":"46b62ccc9e5b9878"}},{"arxiv_id":"2307.11308","paper":"/paper/dpm-ot-a-new-diffusion-probabilistic-model","title":"DPM-OT: A New Diffusion Probabilistic Model Based on Optimal Transport","date":"2023-07-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cognaclee/dpm-ot","path":"datasets/utils.py","file_url":"https://github.com/cognaclee/dpm-ot/blob/HEAD/datasets/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7345763c9b9300fb","mcp_get_code":{"code_sha256":"7345763c9b9300fb"}},{"arxiv_id":"2303.00354","paper":"/paper/unlimited-size-diffusion-restoration","title":"Unlimited-Size Diffusion Restoration","date":"2023-03-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wyhuai/ddnm","path":"datasets/utils.py","file_url":"https://github.com/wyhuai/ddnm/blob/HEAD/datasets/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7345763c9b9300fb","mcp_get_code":{"code_sha256":"7345763c9b9300fb"}},{"arxiv_id":"2301.12686","paper":"/paper/gibbsddrm-a-partially-collapsed-gibbs-sampler","title":"GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration","date":"2023-01-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sony/gibbsddrm","path":"datasets/utils.py","file_url":"https://github.com/sony/gibbsddrm/blob/HEAD/datasets/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7345763c9b9300fb","mcp_get_code":{"code_sha256":"7345763c9b9300fb"}},{"arxiv_id":"2301.07969","paper":"/paper/fast-inference-in-denoising-diffusion-models","title":"Fast Inference in Denoising Diffusion Models via MMD Finetuning","date":"2023-01-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"diegovalsesia/mmd-ddm","path":"datasets/utils.py","file_url":"https://github.com/diegovalsesia/mmd-ddm/blob/HEAD/datasets/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7345763c9b9300fb","mcp_get_code":{"code_sha256":"7345763c9b9300fb"}},{"arxiv_id":"2211.14680","paper":"/paper/a-physics-informed-diffusion-model-for-high","title":"A Physics-informed Diffusion Model for High-fidelity Flow Field Reconstruction","date":"2022-11-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BaratiLab/Diffusion-based-Fluid-Super-resolution","path":"train_ddpm/datasets/utils.py","file_url":"https://github.com/BaratiLab/Diffusion-based-Fluid-Super-resolution/blob/HEAD/train_ddpm/datasets/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7345763c9b9300fb","mcp_get_code":{"code_sha256":"7345763c9b9300fb"}},{"arxiv_id":"2210.11633","paper":"/paper/graphically-structured-diffusion-models","title":"Graphically Structured Diffusion Models","date":"2022-10-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"plai-group/gsdm","path":"datasets/utils.py","file_url":"https://github.com/plai-group/gsdm/blob/HEAD/datasets/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7345763c9b9300fb","mcp_get_code":{"code_sha256":"7345763c9b9300fb"}},{"arxiv_id":"2207.09639","paper":"/paper/dc-bench-dataset-condensation-benchmark","title":"DC-BENCH: Dataset Condensation Benchmark","date":"2022-07-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"justincui03/dc_benchmark","path":"darts-pt/nasbench201/DownsampledImageNet.py","file_url":"https://github.com/justincui03/dc_benchmark/blob/HEAD/darts-pt/nasbench201/DownsampledImageNet.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e947a6effcaaf32b","mcp_get_code":{"code_sha256":"e947a6effcaaf32b"}},{"arxiv_id":"2207.06267","paper":"/paper/task-agnostic-representation-consolidation-a","title":"Task Agnostic Representation Consolidation: a Self-supervised based Continual Learning Approach","date":"2022-07-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"neurai-lab/tarc","path":"datasets/cifar10_noisy.py","file_url":"https://github.com/neurai-lab/tarc/blob/HEAD/datasets/cifar10_noisy.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac9904f665a5702","mcp_get_code":{"code_sha256":"fac9904f665a5702"}},{"arxiv_id":"2206.02262","paper":"/paper/diffusion-gan-training-gans-with-diffusion","title":"Diffusion-GAN: Training GANs with Diffusion","date":"2022-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jegzheng/truncated-diffusion-probabilistic-models","path":"datasets/utils.py","file_url":"https://github.com/jegzheng/truncated-diffusion-probabilistic-models/blob/HEAD/datasets/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7345763c9b9300fb","mcp_get_code":{"code_sha256":"7345763c9b9300fb"}},{"arxiv_id":"2205.05662","paper":"/paper/deep-architecture-connectivity-matters-for","title":"Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained Analysis","date":"2022-05-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenwydj/architecture_convergence","path":"nas-without-training/datasets/DownsampledImageNet.py","file_url":"https://github.com/chenwydj/architecture_convergence/blob/HEAD/nas-without-training/datasets/DownsampledImageNet.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e947a6effcaaf32b","mcp_get_code":{"code_sha256":"e947a6effcaaf32b"}},{"arxiv_id":"2112.08654","paper":"/paper/learning-to-prompt-for-continual-learning-1","title":"Learning to Prompt for Continual Learning","date":"2021-12-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JH-LEE-KR/l2p-pytorch","path":"continual_datasets/dataset_utils.py","file_url":"https://github.com/JH-LEE-KR/l2p-pytorch/blob/HEAD/continual_datasets/dataset_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":false,"code_sha256_prefix":"46b62ccc9e5b9878","mcp_get_code":{"code_sha256":"46b62ccc9e5b9878"}},{"arxiv_id":"2108.11939","paper":"/paper/understanding-and-accelerating-neural","title":"Understanding and Accelerating Neural Architecture Search with Training-Free and Theory-Grounded Metrics","date":"2021-08-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vita-group/tegnas","path":"lib/datasets/DownsampledImageNet.py","file_url":"https://github.com/vita-group/tegnas/blob/HEAD/lib/datasets/DownsampledImageNet.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e947a6effcaaf32b","mcp_get_code":{"code_sha256":"e947a6effcaaf32b"}},{"arxiv_id":"2106.10891","paper":"/paper/open-set-label-noise-can-improve-robustness","title":"Open-set Label Noise Can Improve Robustness Against Inherent Label Noise","date":"2021-06-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hongxin001/ODNL","path":"algorithms/odnl.py","file_url":"https://github.com/hongxin001/ODNL/blob/HEAD/algorithms/odnl.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"379a713ff6573163","mcp_get_code":{"code_sha256":"379a713ff6573163"}},{"arxiv_id":"2106.06799","paper":"/paper/zero-cost-proxies-meet-differentiable","title":"Zero-Cost Operation Scoring in Differentiable Architecture Search","date":"2021-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zerocostptnas/zerocost_operation_score","path":"nasbench201/DownsampledImageNet.py","file_url":"https://github.com/zerocostptnas/zerocost_operation_score/blob/HEAD/nasbench201/DownsampledImageNet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d3f4daf499849800","mcp_get_code":{"code_sha256":"d3f4daf499849800"}},{"arxiv_id":"2106.05390","paper":"/paper/optimizing-reusable-knowledge-for-continual","title":"Optimizing Reusable Knowledge for Continual Learning via Metalearning","date":"2021-06-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JuliousHurtado/meta-training-setup","path":"dataloaders/utils.py","file_url":"https://github.com/JuliousHurtado/meta-training-setup/blob/HEAD/dataloaders/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"46b62ccc9e5b9878","mcp_get_code":{"code_sha256":"46b62ccc9e5b9878"}},{"arxiv_id":"2003.09553","paper":"/paper/adversarial-continual-learning","title":"Adversarial Continual Learning","date":"2020-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/Adversarial-Continual-Learning","path":"src/dataloaders/utils.py","file_url":"https://github.com/facebookresearch/Adversarial-Continual-Learning/blob/HEAD/src/dataloaders/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"46b62ccc9e5b9878","mcp_get_code":{"code_sha256":"46b62ccc9e5b9878"}},{"arxiv_id":"2001.00326","paper":"/paper/nas-bench-102-extending-the-scope-of","title":"NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search","date":"2020-01-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Edge-AI-Acceleration-Lab/RBFleX-NAS","path":"designspace/NAS-Bench-201/DownsampledImageNet.py","file_url":"https://github.com/Edge-AI-Acceleration-Lab/RBFleX-NAS/blob/HEAD/designspace/NAS-Bench-201/DownsampledImageNet.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e947a6effcaaf32b","mcp_get_code":{"code_sha256":"e947a6effcaaf32b"}},{"arxiv_id":"1908.07724","paper":"/paper/190807724","title":"Restricted Recurrent Neural Networks","date":"2019-08-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"diaoenmao/Restricted-Recurrent-Neural-Networks","path":"src/datasets/utils.py","file_url":"https://github.com/diaoenmao/Restricted-Recurrent-Neural-Networks/blob/HEAD/src/datasets/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c37e20d42c55a32a","mcp_get_code":{"code_sha256":"c37e20d42c55a32a"}},{"arxiv_id":"1906.00189","paper":"/paper/190600189","title":"Are Anchor Points Really Indispensable in Label-Noise Learning?","date":"2019-06-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xiaoboxia/T-Revision","path":"utils.py","file_url":"https://github.com/xiaoboxia/T-Revision/blob/HEAD/utils.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac9904f665a5702","mcp_get_code":{"code_sha256":"fac9904f665a5702"}},{"arxiv_id":"1903.12648","paper":"/paper/incremental-learning-with-unlabeled-data-in","title":"Overcoming Catastrophic Forgetting with Unlabeled Data in the Wild","date":"2019-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kibok90/iccv2019-inc","path":"datasets/utils.py","file_url":"https://github.com/kibok90/iccv2019-inc/blob/HEAD/datasets/utils.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac9904f665a5702","mcp_get_code":{"code_sha256":"fac9904f665a5702"}},{"arxiv_id":"1902.01950","paper":"/paper/meta-amortized-variational-inference-and","title":"Meta-Amortized Variational Inference and Learning","date":"2019-02-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mhw32/meta-inference-public","path":"src/datasets/norb.py","file_url":"https://github.com/mhw32/meta-inference-public/blob/HEAD/src/datasets/norb.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac9904f665a5702","mcp_get_code":{"code_sha256":"fac9904f665a5702"}},{"arxiv_id":"aaai_29393","paper":null,"title":"arXiv:aaai_29393","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"VGCQ/DSD2","path":"utils/cifar.py","file_url":"https://github.com/VGCQ/DSD2/blob/HEAD/utils/cifar.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac9904f665a5702","mcp_get_code":{"code_sha256":"fac9904f665a5702"}},{"arxiv_id":"Sun_Unleashing_the_Power_of_Gradient_Signal-to-Noise_Ratio_for_Zero-Shot_NAS_ICCV_2023_paper","paper":null,"title":"arXiv:Sun_Unleashing_the_Power_of_Gradient_Signal-to-Noise_Ratio_for_Zero-Shot_NAS_ICCV_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Sunzh1996/Xi-GSNR","path":"Xi_GSNR_Consistency/datasets/DownsampledImageNet.py","file_url":"https://github.com/Sunzh1996/Xi-GSNR/blob/HEAD/Xi_GSNR_Consistency/datasets/DownsampledImageNet.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e947a6effcaaf32b","mcp_get_code":{"code_sha256":"e947a6effcaaf32b"}},{"arxiv_id":"Fahes_PODA_Prompt-driven_Zero-shot_Domain_Adaptation_ICCV_2023_paper","paper":null,"title":"arXiv:Fahes_PODA_Prompt-driven_Zero-shot_Domain_Adaptation_ICCV_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"astra-vision/PODA","path":"datasets/utils.py","file_url":"https://github.com/astra-vision/PODA/blob/HEAD/datasets/utils.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":"7345763c9b9300fb","mcp_get_code":{"code_sha256":"7345763c9b9300fb"}},{"arxiv_id":"136640364","paper":null,"title":"arXiv:136640364","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"lukashedegaard/co3d","path":"datasets/download.py","file_url":"https://github.com/lukashedegaard/co3d/blob/HEAD/datasets/download.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":"9d650a060048e8e8","mcp_get_code":{"code_sha256":"9d650a060048e8e8"}},{"arxiv_id":"00676","paper":null,"title":"arXiv:00676","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"lliai/Auto-DAS","path":"autodas/datasets/imagenet16.py","file_url":"https://github.com/lliai/Auto-DAS/blob/HEAD/autodas/datasets/imagenet16.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":"24aee327ca41fde9","mcp_get_code":{"code_sha256":"24aee327ca41fde9"}}]}