{"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/deconv","entry":"deconv","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":48,"n_papers_ran":31,"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":29,"n_samples_ran":13,"n_samples_fingerprinted":2,"n_places":50,"n_places_pointer_only":17,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":8,"ran_fixture":0,"ran":5,"unverified":16},"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":"2603.15129","paper":"/paper/arxiv-2603-15129","title":"Next-Frame Decoding for Ultra-Low-Bitrate Image Compression with Video Diffusion Priors","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"UnoC-727/NeFIC","path":"nefic/anchor_codec/layers/conv.py","file_url":"https://github.com/UnoC-727/NeFIC/blob/HEAD/nefic/anchor_codec/layers/conv.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d7a1893b6b1fae5c","mcp_get_code":{"code_sha256":"d7a1893b6b1fae5c"}},{"arxiv_id":"2510.12479","paper":"/paper/arxiv-2510-12479","title":"MH-LVC: Multi-Hypothesis Temporal Prediction for Learned Conditional Residual Video Coding","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"NYCU-MAPL/MHLVC","path":"compressai/RIFE/IFNet.py","file_url":"https://github.com/NYCU-MAPL/MHLVC/blob/HEAD/compressai/RIFE/IFNet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"612249f14f261920","mcp_get_code":{"code_sha256":"612249f14f261920"}},{"arxiv_id":"2506.15201","paper":null,"title":"arXiv:2506.15201","date":null,"month_inferred_from_arxiv_id":"2025-06","title_source":null,"repo":"JiayinXu5499/PSIC","path":"models/hy_cond.py","file_url":"https://github.com/JiayinXu5499/PSIC/blob/HEAD/models/hy_cond.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1afd37c576493fea","mcp_get_code":{"code_sha256":"1afd37c576493fea"}},{"arxiv_id":"2502.05741","paper":"/paper/linear-attention-modeling-for-learned-image","title":"Linear Attention Modeling for Learned Image Compression","date":"2025-02-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sjtu-medialab/RwkvCompress","path":"models/lalic.py","file_url":"https://github.com/sjtu-medialab/RwkvCompress/blob/HEAD/models/lalic.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1afd37c576493fea","mcp_get_code":{"code_sha256":"1afd37c576493fea"}},{"arxiv_id":"2412.00596","paper":"/paper/phyt2v-llm-guided-iterative-self-refinement","title":"PhyT2V: LLM-Guided Iterative Self-Refinement for Physics-Grounded Text-to-Video Generation","date":"2024-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pittisl/phyt2v","path":"inference/gradio_composite_demo/rife/IFNet.py","file_url":"https://github.com/pittisl/phyt2v/blob/HEAD/inference/gradio_composite_demo/rife/IFNet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"48ae7f74e7480412","mcp_get_code":{"code_sha256":"48ae7f74e7480412"}},{"arxiv_id":"2410.04847","paper":"/paper/causal-context-adjustment-loss-for-learned","title":"Causal Context Adjustment Loss for Learned Image Compression","date":"2024-10-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LabShuHangGU/CCA","path":"models/aux_em.py","file_url":"https://github.com/LabShuHangGU/CCA/blob/HEAD/models/aux_em.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1afd37c576493fea","mcp_get_code":{"code_sha256":"1afd37c576493fea"}},{"arxiv_id":"2407.16308","paper":"/paper/safnet-selective-alignment-fusion-network-for","title":"SAFNet: Selective Alignment Fusion Network for Efficient HDR Imaging","date":"2024-07-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ltkong218/SAFNet","path":"models/SAFNet.py","file_url":"https://github.com/ltkong218/SAFNet/blob/HEAD/models/SAFNet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1cea7934f596790b","mcp_get_code":{"code_sha256":"1cea7934f596790b"}},{"arxiv_id":"2407.10831","paper":"/paper/temporal-event-stereo-via-joint-learning-with","title":"Temporal Event Stereo via Joint Learning with Stereoscopic Flow","date":"2024-07-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mickeykang16/temporaleventstereo","path":"models/model_large.py","file_url":"https://github.com/mickeykang16/temporaleventstereo/blob/HEAD/models/model_large.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5ab284f1473d2ba8","mcp_get_code":{"code_sha256":"5ab284f1473d2ba8"}},{"arxiv_id":"2407.02315","paper":"/paper/vfimamba-video-frame-interpolation-with-state","title":"VFIMamba: Video Frame Interpolation with State Space Models","date":"2024-07-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mcg-nju/vfimamba","path":"model/refine.py","file_url":"https://github.com/mcg-nju/vfimamba/blob/HEAD/model/refine.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":"23f41c2655d6295a","mcp_get_code":{"code_sha256":"23f41c2655d6295a"}},{"arxiv_id":"2307.15421","paper":"/paper/mlic-linear-complexity-multi-reference","title":"MLIC++: Linear Complexity Multi-Reference Entropy Modeling for Learned Image Compression","date":"2023-07-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiangweibeta/mlic","path":"MLIC++/modules/layers/conv.py","file_url":"https://github.com/jiangweibeta/mlic/blob/HEAD/MLIC%2B%2B/modules/layers/conv.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"1afd37c576493fea","mcp_get_code":{"code_sha256":"1afd37c576493fea"}},{"arxiv_id":"2306.05671","paper":"/paper/topology-aware-uncertainty-for-image-1","title":"Topology-Aware Uncertainty for Image Segmentation","date":"2023-06-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"iMED-Lab/CS-Net","path":"model/csnet.py","file_url":"https://github.com/iMED-Lab/CS-Net/blob/HEAD/model/csnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b528298ef3ced2c0","mcp_get_code":{"code_sha256":"b528298ef3ced2c0"}},{"arxiv_id":"2305.10808","paper":"/paper/manifold-aware-self-training-for-unsupervised","title":"Manifold-Aware Self-Training for Unsupervised Domain Adaptation on Regressing 6D Object Pose","date":"2023-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Gorilla-Lab-SCUT/MAST","path":"MAST/models/flownet.py","file_url":"https://github.com/Gorilla-Lab-SCUT/MAST/blob/HEAD/MAST/models/flownet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"05aaa6b1fc1c8968","mcp_get_code":{"code_sha256":"05aaa6b1fc1c8968"}},{"arxiv_id":"2304.13596","paper":"/paper/video-frame-interpolation-with-densely","title":"Video Frame Interpolation with Densely Queried Bilateral Correlation","date":"2023-04-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kinoud/DQBC","path":"models/dqbc/flow_tea/IFNet.py","file_url":"https://github.com/kinoud/DQBC/blob/HEAD/models/dqbc/flow_tea/IFNet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"612249f14f261920","mcp_get_code":{"code_sha256":"612249f14f261920"}},{"arxiv_id":"2211.06018","paper":"/paper/mdflow-unsupervised-optical-flow-learning-by","title":"MDFlow: Unsupervised Optical Flow Learning by Reliable Mutual Knowledge Distillation","date":"2022-11-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"dc4fb7628379691e","mcp_get_code":{"code_sha256":"dc4fb7628379691e"}},{"arxiv_id":"2209.08430","paper":"/paper/dytanvo-joint-refinement-of-visual-odometry","title":"DytanVO: Joint Refinement of Visual Odometry and Motion Segmentation in Dynamic Environments","date":"2022-09-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"geniussh/dytanvo","path":"Network/PWC/PWCNet.py","file_url":"https://github.com/geniussh/dytanvo/blob/HEAD/Network/PWC/PWCNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"dc4fb7628379691e","mcp_get_code":{"code_sha256":"dc4fb7628379691e"}},{"arxiv_id":"2203.10886","paper":"/paper/elic-efficient-learned-image-compression-with","title":"ELIC: Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Coding","date":"2022-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JiangWeibeta/ELIC","path":"modules/layers/conv.py","file_url":"https://github.com/JiangWeibeta/ELIC/blob/HEAD/modules/layers/conv.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"1afd37c576493fea","mcp_get_code":{"code_sha256":"1afd37c576493fea"}},{"arxiv_id":"2109.02974","paper":"/paper/fuseformer-fusing-fine-grained-information-in","title":"FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting","date":"2021-09-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ruiliu-ai/FuseFormer","path":"model/fuseformer.py","file_url":"https://github.com/ruiliu-ai/FuseFormer/blob/HEAD/model/fuseformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e73d218a85e7f736","mcp_get_code":{"code_sha256":"e73d218a85e7f736"}},{"arxiv_id":"2108.09551","paper":"/paper/variable-rate-deep-image-compression-through","title":"Variable-Rate Deep Image Compression through Spatially-Adaptive Feature Transform","date":"2021-08-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"micmic123/qmapcompression","path":"models/models.py","file_url":"https://github.com/micmic123/qmapcompression/blob/HEAD/models/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1afd37c576493fea","mcp_get_code":{"code_sha256":"1afd37c576493fea"}},{"arxiv_id":"2108.06815","paper":"/paper/asymmetric-bilateral-motion-estimation-for","title":"Asymmetric Bilateral Motion Estimation for Video Frame Interpolation","date":"2021-08-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JunHeum/ABME","path":"model/SBMNet.py","file_url":"https://github.com/JunHeum/ABME/blob/HEAD/model/SBMNet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1cea7934f596790b","mcp_get_code":{"code_sha256":"1cea7934f596790b"}},{"arxiv_id":"2106.01970","paper":"/paper/nerfactor-neural-factorization-of-shape-and","title":"NeRFactor: Neural Factorization of Shape and Reflectance Under an Unknown Illumination","date":"2021-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"google/nerfactor","path":"nerfactor/networks/layers.py","file_url":"https://github.com/google/nerfactor/blob/HEAD/nerfactor/networks/layers.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":"2701f7ec7df9aaa0","mcp_get_code":{"code_sha256":"2701f7ec7df9aaa0"}},{"arxiv_id":"2103.07362","paper":"/paper/plade-net-towards-pixel-level-accuracy-for","title":"PLADE-Net: Towards Pixel-Level Accuracy for Self-Supervised Single-View Depth Estimation with Neural Positional Encoding and Distilled Matting Loss","date":"2021-03-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JuanLuisGonzalez/PLADE-net","path":"models/FAL_net2B_gep2s.py","file_url":"https://github.com/JuanLuisGonzalez/PLADE-net/blob/HEAD/models/FAL_net2B_gep2s.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"638d8821893f4404","mcp_get_code":{"code_sha256":"638d8821893f4404"}},{"arxiv_id":"2103.04524","paper":"/paper/fastflownet-a-lightweight-network-for-fast","title":"FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation","date":"2021-03-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"dc4fb7628379691e","mcp_get_code":{"code_sha256":"dc4fb7628379691e"}},{"arxiv_id":"2101.04777","paper":"/paper/binary-ttc-a-temporal-geofence-for-autonomous","title":"Binary TTC: A Temporal Geofence for Autonomous Navigation","date":"2021-01-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"NVlabs/BiTTC","path":"src/models/BiTTCNet.py","file_url":"https://github.com/NVlabs/BiTTC/blob/HEAD/src/models/BiTTCNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"0332dcda39bb8220","mcp_get_code":{"code_sha256":"0332dcda39bb8220"}},{"arxiv_id":"2011.00359","paper":"/paper/tartanvo-a-generalizable-learning-based-vo","title":"TartanVO: A Generalizable Learning-based VO","date":"2020-10-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"castacks/tartanvo","path":"Network/PWC/PWCNet.py","file_url":"https://github.com/castacks/tartanvo/blob/HEAD/Network/PWC/PWCNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"dc4fb7628379691e","mcp_get_code":{"code_sha256":"dc4fb7628379691e"}},{"arxiv_id":"2009.07823","paper":"/paper/gocor-bringing-globally-optimized","title":"GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network","date":"2020-09-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PruneTruong/GLU-Net","path":"models/our_models/GLUNet.py","file_url":"https://github.com/PruneTruong/GLU-Net/blob/HEAD/models/our_models/GLUNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"e5cf22998f71a87e","mcp_get_code":{"code_sha256":"e5cf22998f71a87e"}},{"arxiv_id":"2009.07378","paper":"/paper/bop-challenge-2020-on-6d-object-localization","title":"BOP Challenge 2020 on 6D Object Localization","date":"2020-09-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"azad96/cosypose-custom","path":"cosypose/models/flownet.py","file_url":"https://github.com/azad96/cosypose-custom/blob/HEAD/cosypose/models/flownet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"05aaa6b1fc1c8968","mcp_get_code":{"code_sha256":"05aaa6b1fc1c8968"}},{"arxiv_id":"2007.12622","paper":"/paper/bmbc-bilateral-motion-estimation-with","title":"BMBC:Bilateral Motion Estimation with Bilateral Cost Volume for Video Interpolation","date":"2020-07-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JunHeum/BMBC","path":"model/BMNet.py","file_url":"https://github.com/JunHeum/BMBC/blob/HEAD/model/BMNet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1cea7934f596790b","mcp_get_code":{"code_sha256":"1cea7934f596790b"}},{"arxiv_id":"2007.05201","paper":"/paper/rose-a-retinal-oct-angiography-vessel","title":"ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model","date":"2020-07-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"iMED-Lab/OCTA-Net-OCTA-Vessel-Segmentation-Network","path":"code/OCTA-Net/imed_models.py","file_url":"https://github.com/iMED-Lab/OCTA-Net-OCTA-Vessel-Segmentation-Network/blob/HEAD/code/OCTA-Net/imed_models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"b528298ef3ced2c0","mcp_get_code":{"code_sha256":"b528298ef3ced2c0"}},{"arxiv_id":"1911.08655","paper":"/paper/towards-physics-informed-deep-learning-for-1","title":"Towards Physics-informed Deep Learning for Turbulent Flow Prediction","date":"2019-11-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Rose-STL-Lab/Turbulent-Flow-Net","path":"models/model.py","file_url":"https://github.com/Rose-STL-Lab/Turbulent-Flow-Net/blob/HEAD/models/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7532429cea03119b","mcp_get_code":{"code_sha256":"7532429cea03119b"}},{"arxiv_id":"1909.05452","paper":"/paper/flow-motion-and-depth-network-for-monocular","title":"Flow-Motion and Depth Network for Monocular Stereo and Beyond","date":"2019-09-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"dc4fb7628379691e","mcp_get_code":{"code_sha256":"dc4fb7628379691e"}},{"arxiv_id":"1907.11628","paper":"/paper/unsupervised-learning-for-optical-flow","title":"Unsupervised Learning for Optical Flow Estimation Using Pyramid Convolution LSTM","date":"2019-07-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Kwanss/PCLNet","path":"models/PCLNet.py","file_url":"https://github.com/Kwanss/PCLNet/blob/HEAD/models/PCLNet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e4efe7b05aaf1aff","mcp_get_code":{"code_sha256":"e4efe7b05aaf1aff"}},{"arxiv_id":"1906.11557","paper":"/paper/flexible-svbrdf-capture-with-a-multi-image","title":"Flexible SVBRDF Capture with a Multi-Image Deep Network","date":null,"month_inferred_from_arxiv_id":"2019-06","title_source":"archive","repo":"valentin-deschaintre/multi-image-deepNet-SVBRDF-acquisition","path":"tfHelpers.py","file_url":"https://github.com/valentin-deschaintre/multi-image-deepNet-SVBRDF-acquisition/blob/HEAD/tfHelpers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0cb6d43ff89d5ab1","mcp_get_code":{"code_sha256":"0cb6d43ff89d5ab1"}},{"arxiv_id":"1906.01529","paper":"/paper/generative-adversarial-networks-a-survey-and","title":"Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy","date":"2019-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wileyw/DeepLearningDemos","path":"CycleGANSolution/a4-code-v2-updated/models.py","file_url":"https://github.com/wileyw/DeepLearningDemos/blob/HEAD/CycleGANSolution/a4-code-v2-updated/models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9aeba2d3f55784ae","mcp_get_code":{"code_sha256":"9aeba2d3f55784ae"}},{"arxiv_id":"1905.01270","paper":"/paper/drit-diverse-image-to-image-translation-via","title":"DRIT++: Diverse Image-to-Image Translation via Disentangled Representations","date":"2019-05-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"taki0112/DRIT-Tensorflow","path":"ops.py","file_url":"https://github.com/taki0112/DRIT-Tensorflow/blob/HEAD/ops.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6968524cd9833f53","mcp_get_code":{"code_sha256":"6968524cd9833f53"}},{"arxiv_id":"1904.00830","paper":"/paper/depth-aware-video-frame-interpolation","title":"Depth-Aware Video Frame Interpolation","date":"2019-04-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"baowenbo/DAIN","path":"PWCNet/PWCNet.py","file_url":"https://github.com/baowenbo/DAIN/blob/HEAD/PWCNet/PWCNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dc4fb7628379691e","mcp_get_code":{"code_sha256":"dc4fb7628379691e"}},{"arxiv_id":"1903.11412","paper":"/paper/self-supervised-learning-via-conditional","title":"Self-Supervised Learning via Conditional Motion Propagation","date":"2019-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"XiaohangZhan/conditional-motion-propagation","path":"models/modules/decoder.py","file_url":"https://github.com/XiaohangZhan/conditional-motion-propagation/blob/HEAD/models/modules/decoder.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dfb9a0605c0f4784","mcp_get_code":{"code_sha256":"dfb9a0605c0f4784"}},{"arxiv_id":"1812.11647","paper":"/paper/path-invariant-map-networks","title":"Path-Invariant Map Networks","date":"2018-12-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zaiweizhang/path_invariance_map_network","path":"ops.py","file_url":"https://github.com/zaiweizhang/path_invariance_map_network/blob/HEAD/ops.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":"8f90e1aa22b5dec8","mcp_get_code":{"code_sha256":"8f90e1aa22b5dec8"}},{"arxiv_id":"1810.10510","paper":"/paper/neighbourhood-consensus-networks","title":"Neighbourhood Consensus Networks","date":"2018-10-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JiwonCocoder/-Joint-Learning-of-Feature-Extraction-and-Cost-Aggregation-for-Semantic-Matching","path":"lib/matching_model.py","file_url":"https://github.com/JiwonCocoder/-Joint-Learning-of-Feature-Extraction-and-Cost-Aggregation-for-Semantic-Matching/blob/HEAD/lib/matching_model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dc4fb7628379691e","mcp_get_code":{"code_sha256":"dc4fb7628379691e"}},{"arxiv_id":"1810.10191","paper":"/paper/making-sense-of-vision-and-touch-self","title":"Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks","date":"2018-10-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"stanford-iprl-lab/multimodal_representation","path":"multimodal/models/base_models/layers.py","file_url":"https://github.com/stanford-iprl-lab/multimodal_representation/blob/HEAD/multimodal/models/base_models/layers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"05aaa6b1fc1c8968","mcp_get_code":{"code_sha256":"05aaa6b1fc1c8968"}},{"arxiv_id":"1807.09190","paper":"/paper/premvos-proposal-generation-refinement-and","title":"PReMVOS: Proposal-generation, Refinement and Merging for Video Object Segmentation","date":"2018-07-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"dc4fb7628379691e","mcp_get_code":{"code_sha256":"dc4fb7628379691e"}},{"arxiv_id":"1807.00734","paper":"/paper/the-relativistic-discriminator-a-key-element","title":"The relativistic discriminator: a key element missing from standard GAN","date":"2018-07-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"taki0112/RelativisticGAN-Tensorflow","path":"ops.py","file_url":"https://github.com/taki0112/RelativisticGAN-Tensorflow/blob/HEAD/ops.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4700cd22b8033044","mcp_get_code":{"code_sha256":"4700cd22b8033044"}},{"arxiv_id":"1710.10196","paper":"/paper/progressive-growing-of-gans-for-improved","title":"Progressive Growing of GANs for Improved Quality, Stability, and Variation","date":"2017-10-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"claushofmann/GAN-game-art","path":"progressive_gan.py","file_url":"https://github.com/claushofmann/GAN-game-art/blob/HEAD/progressive_gan.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fbcbe989dbb6f48f","mcp_get_code":{"code_sha256":"fbcbe989dbb6f48f"}},{"arxiv_id":"1710.10196","paper":"/paper/progressive-growing-of-gans-for-improved","title":"Progressive Growing of GANs for Improved Quality, Stability, and Variation","date":"2017-10-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nashory/pggan-pytorch","path":"network.py","file_url":"https://github.com/nashory/pggan-pytorch/blob/HEAD/network.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"69c226eb82fee692","mcp_get_code":{"code_sha256":"69c226eb82fee692"}},{"arxiv_id":"1709.02371","paper":"/paper/pwc-net-cnns-for-optical-flow-using-pyramid","title":"PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume","date":"2017-09-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yanqi1811/PWC-Net","path":"PyTorch/models/PWCNet.py","file_url":"https://github.com/yanqi1811/PWC-Net/blob/HEAD/PyTorch/models/PWCNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"dc4fb7628379691e","mcp_get_code":{"code_sha256":"dc4fb7628379691e"}},{"arxiv_id":"1606.01583","paper":"/paper/semi-supervised-learning-with-generative","title":"Semi-Supervised Learning with Generative Adversarial Networks","date":"2016-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yunjey/mnist-svhn-transfer","path":"model.py","file_url":"https://github.com/yunjey/mnist-svhn-transfer/blob/HEAD/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"295a50c6d7938efd","mcp_get_code":{"code_sha256":"295a50c6d7938efd"}},{"arxiv_id":"1603.08155","paper":"/paper/perceptual-losses-for-real-time-style","title":"Perceptual Losses for Real-Time Style Transfer and Super-Resolution","date":"2016-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ZyoungXu/MoSt-DSA","path":"model/refine.py","file_url":"https://github.com/ZyoungXu/MoSt-DSA/blob/HEAD/model/refine.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":"23f41c2655d6295a","mcp_get_code":{"code_sha256":"23f41c2655d6295a"}},{"arxiv_id":"1603.08155","paper":"/paper/perceptual-losses-for-real-time-style","title":"Perceptual Losses for Real-Time Style Transfer and Super-Resolution","date":"2016-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thatbrguy/Dehaze-GAN","path":"legacy/utils.py","file_url":"https://github.com/thatbrguy/Dehaze-GAN/blob/HEAD/legacy/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"288caa152d503142","mcp_get_code":{"code_sha256":"288caa152d503142"}},{"arxiv_id":"1505.04597","paper":"/paper/u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tanyanair/segmentation_uncertainty","path":"bunet/models/bunet.py","file_url":"https://github.com/tanyanair/segmentation_uncertainty/blob/HEAD/bunet/models/bunet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e7bac554c43cd07c","mcp_get_code":{"code_sha256":"e7bac554c43cd07c"}},{"arxiv_id":"Sun_Pixel-level_Semantic_Correspondence_through_Layout-aware_Representation_Learning_and_Multi-scale_Matching_CVPR_2024_paper","paper":null,"title":"arXiv:Sun_Pixel-level_Semantic_Correspondence_through_Layout-aware_Representation_Learning_and_Multi-scale_Matching_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"YXSUNMADMAX/LPMFlow","path":"models/mod.py","file_url":"https://github.com/YXSUNMADMAX/LPMFlow/blob/HEAD/models/mod.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"dc4fb7628379691e","mcp_get_code":{"code_sha256":"dc4fb7628379691e"}},{"arxiv_id":"136760331","paper":null,"title":"arXiv:136760331","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"chang9711/BIPS","path":"KCVS/TA_session/custom_layers.py","file_url":"https://github.com/chang9711/BIPS/blob/HEAD/KCVS/TA_session/custom_layers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e36c519cb822774d","mcp_get_code":{"code_sha256":"e36c519cb822774d"}}]}