{"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/spatial-average","entry":"spatial_average","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":34,"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":6,"n_samples_ran":3,"n_samples_fingerprinted":3,"n_places":34,"n_places_pointer_only":11,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":2,"ran_fixture":0,"ran":1,"unverified":3},"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":"2508.16121","paper":"/paper/arxiv-2508-16121","title":"Lightweight and Fast Real-time Image Enhancement via Decomposition of the Spatial-aware Lookup Tables","date":null,"month_inferred_from_arxiv_id":"2025-08","title_source":"syntology","repo":"WontaeaeKim/SVDLUT","path":"lpips/lpips.py","file_url":"https://github.com/WontaeaeKim/SVDLUT/blob/HEAD/lpips/lpips.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"29e5e72bcd006dcd","mcp_get_code":{"code_sha256":"29e5e72bcd006dcd"}},{"arxiv_id":"2502.01591","paper":"/paper/improving-transformer-world-models-for-data","title":"Improving Transformer World Models for Data-Efficient RL","date":"2025-02-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eloialonso/iris","path":"src/models/tokenizer/tokenizer.py","file_url":"https://github.com/eloialonso/iris/blob/HEAD/src/models/tokenizer/tokenizer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"6855e475947477db","mcp_get_code":{"code_sha256":"6855e475947477db"}},{"arxiv_id":"2412.09013","paper":"/paper/arbitrary-steps-image-super-resolution-via","title":"Arbitrary-steps Image Super-resolution via Diffusion Inversion","date":"2024-12-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zsyoaoa/invsr","path":"latent_lpips/lpips.py","file_url":"https://github.com/zsyoaoa/invsr/blob/HEAD/latent_lpips/lpips.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"29e5e72bcd006dcd","mcp_get_code":{"code_sha256":"29e5e72bcd006dcd"}},{"arxiv_id":"2412.06424","paper":"/paper/deblur4dgs-4d-gaussian-splatting-from-blurry","title":"Deblur4DGS: 4D Gaussian Splatting from Blurry Monocular Video","date":"2024-12-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zcsrenlongz/deblur4dgs","path":"models/networks_basic.py","file_url":"https://github.com/zcsrenlongz/deblur4dgs/blob/HEAD/models/networks_basic.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a4d02c500a136258","mcp_get_code":{"code_sha256":"a4d02c500a136258"}},{"arxiv_id":"2410.02640","paper":"/paper/diffusion-based-extreme-image-compression","title":"RDEIC: Accelerating Diffusion-Based Extreme Image Compression with Relay Residual Diffusion","date":"2024-10-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huai-chang/rdeic","path":"model/lpips.py","file_url":"https://github.com/huai-chang/rdeic/blob/HEAD/model/lpips.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e268053216b1dd62","mcp_get_code":{"code_sha256":"e268053216b1dd62"}},{"arxiv_id":"2408.16450","paper":"/paper/what-to-preserve-and-what-to-transfer","title":"What to Preserve and What to Transfer: Faithful, Identity-Preserving Diffusion-based Hairstyle Transfer","date":"2024-08-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cychungg/hairfusion","path":"eval_models/networks_basic.py","file_url":"https://github.com/cychungg/hairfusion/blob/HEAD/eval_models/networks_basic.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"29e5e72bcd006dcd","mcp_get_code":{"code_sha256":"29e5e72bcd006dcd"}},{"arxiv_id":"2407.08447","paper":"/paper/wildgaussians-3d-gaussian-splatting-in-the","title":"WildGaussians: 3D Gaussian Splatting in the Wild","date":"2024-07-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jkulhanek/wild-gaussians","path":"wildgaussians/_metrics_lpips.py","file_url":"https://github.com/jkulhanek/wild-gaussians/blob/HEAD/wildgaussians/_metrics_lpips.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"29e5e72bcd006dcd","mcp_get_code":{"code_sha256":"29e5e72bcd006dcd"}},{"arxiv_id":"2406.09750","paper":"/paper/controlvar-exploring-controllable-visual","title":"ControlVAR: Exploring Controllable Visual Autoregressive Modeling","date":"2024-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lxa9867/ControlVAR","path":"losses/lpips.py","file_url":"https://github.com/lxa9867/ControlVAR/blob/HEAD/losses/lpips.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e268053216b1dd62","mcp_get_code":{"code_sha256":"e268053216b1dd62"}},{"arxiv_id":"2403.07874","paper":"/paper/beyond-text-frozen-large-language-models-in","title":"Beyond Text: Frozen Large Language Models in Visual Signal Comprehension","date":"2024-03-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zh460045050/V2L-Tokenizer","path":"models/models_v2l.py","file_url":"https://github.com/zh460045050/V2L-Tokenizer/blob/HEAD/models/models_v2l.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e268053216b1dd62","mcp_get_code":{"code_sha256":"e268053216b1dd62"}},{"arxiv_id":"2401.08573","paper":"/paper/benchmarking-the-robustness-of-image","title":"WAVES: Benchmarking the Robustness of Image Watermarks","date":"2024-01-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"29e5e72bcd006dcd","mcp_get_code":{"code_sha256":"29e5e72bcd006dcd"}},{"arxiv_id":"2312.01725","paper":"/paper/stableviton-learning-semantic-correspondence","title":"StableVITON: Learning Semantic Correspondence with Latent Diffusion Model for Virtual Try-On","date":"2023-12-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rlawjdghek/stableviton","path":"eval_models/networks_basic.py","file_url":"https://github.com/rlawjdghek/stableviton/blob/HEAD/eval_models/networks_basic.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"29e5e72bcd006dcd","mcp_get_code":{"code_sha256":"29e5e72bcd006dcd"}},{"arxiv_id":"2310.10651","paper":"/paper/hairclipv2-unifying-hair-editing-via-proxy-1","title":"HairCLIPv2: Unifying Hair Editing via Proxy Feature Blending","date":"2023-10-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wty-ustc/HairCLIPv2","path":"criteria/lpips/networks_basic.py","file_url":"https://github.com/wty-ustc/HairCLIPv2/blob/HEAD/criteria/lpips/networks_basic.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"29e5e72bcd006dcd","mcp_get_code":{"code_sha256":"29e5e72bcd006dcd"}},{"arxiv_id":"2308.07228","paper":"/paper/restoreformer-towards-real-world-blind-face","title":"RestoreFormer++: Towards Real-World Blind Face Restoration from Undegraded Key-Value Pairs","date":"2023-08-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wzhouxiff/restoreformerplusplus","path":"RestoreFormer/modules/losses/lpips.py","file_url":"https://github.com/wzhouxiff/restoreformerplusplus/blob/HEAD/RestoreFormer/modules/losses/lpips.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":"e268053216b1dd62","mcp_get_code":{"code_sha256":"e268053216b1dd62"}},{"arxiv_id":"2307.15157","paper":"/paper/r-lpips-an-adversarially-robust-perceptual","title":"R-LPIPS: An Adversarially Robust Perceptual Similarity Metric","date":"2023-07-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"saraghazanfari/r-lpips","path":"lpips/lpips.py","file_url":"https://github.com/saraghazanfari/r-lpips/blob/HEAD/lpips/lpips.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"29e5e72bcd006dcd","mcp_get_code":{"code_sha256":"29e5e72bcd006dcd"}},{"arxiv_id":"2305.13607","paper":"/paper/not-all-image-regions-matter-masked-vector-1","title":"Not All Image Regions Matter: Masked Vector Quantization for Autoregressive Image Generation","date":"2023-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"crossmodalgroup/maskedvectorquantization","path":"modules/losses/lpips.py","file_url":"https://github.com/crossmodalgroup/maskedvectorquantization/blob/HEAD/modules/losses/lpips.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e268053216b1dd62","mcp_get_code":{"code_sha256":"e268053216b1dd62"}},{"arxiv_id":"2302.05543","paper":"/paper/adding-conditional-control-to-text-to-image","title":"Adding Conditional Control to Text-to-Image Diffusion Models","date":"2023-02-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"explainingai-code/ControlNet-PyTorch","path":"models/lpips.py","file_url":"https://github.com/explainingai-code/ControlNet-PyTorch/blob/HEAD/models/lpips.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"29e5e72bcd006dcd","mcp_get_code":{"code_sha256":"29e5e72bcd006dcd"}},{"arxiv_id":"2301.04634","paper":"/paper/street-view-image-generation-from-a-bird-s","title":"Street-View Image Generation from a Bird's-Eye View Layout","date":"2023-01-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alexanderswerdlow/BEVGen","path":"multi_view_generation/modules/losses/lpips.py","file_url":"https://github.com/alexanderswerdlow/BEVGen/blob/HEAD/multi_view_generation/modules/losses/lpips.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e268053216b1dd62","mcp_get_code":{"code_sha256":"e268053216b1dd62"}},{"arxiv_id":"2207.13686","paper":"/paper/shift-tolerant-perceptual-similarity-metric-1","title":"Shift-tolerant Perceptual Similarity Metric","date":"2022-07-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"abhijay9/shifttolerant-lpips","path":"stlpips/lpips.py","file_url":"https://github.com/abhijay9/shifttolerant-lpips/blob/HEAD/stlpips/lpips.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"29e5e72bcd006dcd","mcp_get_code":{"code_sha256":"29e5e72bcd006dcd"}},{"arxiv_id":"2206.10789","paper":"/paper/scaling-autoregressive-models-for-content","title":"Scaling Autoregressive Models for Content-Rich Text-to-Image Generation","date":"2022-06-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"syang-lab/Pathway_Autoregressive_Text2Image_Model","path":"Parti_Pytorch_V5/parti/losses/lpips.py","file_url":"https://github.com/syang-lab/Pathway_Autoregressive_Text2Image_Model/blob/HEAD/Parti_Pytorch_V5/parti/losses/lpips.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8afa5135930de8c8","mcp_get_code":{"code_sha256":"8afa5135930de8c8"}},{"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":"loss/lpips_loss.py","file_url":"https://github.com/NVlabs/CoordGAN/blob/HEAD/loss/lpips_loss.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e268053216b1dd62","mcp_get_code":{"code_sha256":"e268053216b1dd62"}},{"arxiv_id":"2203.10897","paper":"/paper/unified-multivariate-gaussian-mixture-for","title":"Unified Multivariate Gaussian Mixture for Efficient Neural Image Compression","date":"2022-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xiaosu-zhu/McQuic","path":"mcquic/loss/lpips.py","file_url":"https://github.com/xiaosu-zhu/McQuic/blob/HEAD/mcquic/loss/lpips.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":"e268053216b1dd62","mcp_get_code":{"code_sha256":"e268053216b1dd62"}},{"arxiv_id":"2112.10752","paper":"/paper/high-resolution-image-synthesis-with-latent","title":"High-Resolution Image Synthesis with Latent Diffusion Models","date":"2021-12-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"explainingai-code/StableDiffusion-PyTorch","path":"models/lpips.py","file_url":"https://github.com/explainingai-code/StableDiffusion-PyTorch/blob/HEAD/models/lpips.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"29e5e72bcd006dcd","mcp_get_code":{"code_sha256":"29e5e72bcd006dcd"}},{"arxiv_id":"2112.00384","paper":"/paper/translation-equivariant-image-quantizer-for","title":"Exploration into Translation-Equivariant Image Quantization","date":"2021-12-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wcshin-git/te-vqgan","path":"taming/modules/losses/lpips.py","file_url":"https://github.com/wcshin-git/te-vqgan/blob/HEAD/taming/modules/losses/lpips.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e268053216b1dd62","mcp_get_code":{"code_sha256":"e268053216b1dd62"}},{"arxiv_id":"2110.15678","paper":"/paper/a-shading-guided-generative-implicit-model","title":"A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis","date":"2021-10-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xingangpan/shadegan","path":"lpips/networks_basic.py","file_url":"https://github.com/xingangpan/shadegan/blob/HEAD/lpips/networks_basic.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"29e5e72bcd006dcd","mcp_get_code":{"code_sha256":"29e5e72bcd006dcd"}},{"arxiv_id":"2106.12423","paper":"/paper/alias-free-generative-adversarial-networks","title":"Alias-Free Generative Adversarial Networks","date":"2021-06-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jychoi118/toward_spatial_unbiased","path":"lpips/networks_basic.py","file_url":"https://github.com/jychoi118/toward_spatial_unbiased/blob/HEAD/lpips/networks_basic.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"29e5e72bcd006dcd","mcp_get_code":{"code_sha256":"29e5e72bcd006dcd"}},{"arxiv_id":"2103.16194","paper":"/paper/differentiable-drawing-and-sketching","title":"Differentiable Drawing and Sketching","date":"2021-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jonhare/DifferentiableSketching","path":"dsketch/experiments/shared/args_losses.py","file_url":"https://github.com/jonhare/DifferentiableSketching/blob/HEAD/dsketch/experiments/shared/args_losses.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":"9b40691985d4c7c9","mcp_get_code":{"code_sha256":"9b40691985d4c7c9"}},{"arxiv_id":"2006.09461","paper":"/paper/robust-compressed-sensing-of-generative","title":"Robust Compressed Sensing using Generative Models","date":"2020-06-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ajiljalal/csgm-robust-neurips","path":"src/lpips/networks_basic.py","file_url":"https://github.com/ajiljalal/csgm-robust-neurips/blob/HEAD/src/lpips/networks_basic.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"29e5e72bcd006dcd","mcp_get_code":{"code_sha256":"29e5e72bcd006dcd"}},{"arxiv_id":"2005.05999","paper":"/paper/fast-deep-multi-patch-hierarchical-network","title":"Fast Deep Multi-patch Hierarchical Network for Nonhomogeneous Image Dehazing","date":"2020-05-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"saikatdutta/Stacked_DMSHN_bokeh","path":"PerceptualSimilarity/models/networks_basic.py","file_url":"https://github.com/saikatdutta/Stacked_DMSHN_bokeh/blob/HEAD/PerceptualSimilarity/models/networks_basic.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"29e5e72bcd006dcd","mcp_get_code":{"code_sha256":"29e5e72bcd006dcd"}},{"arxiv_id":"2002.09219","paper":"/paper/stochastic-latent-residual-video-prediction-1","title":"Stochastic Latent Residual Video Prediction","date":"2020-02-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"edouardelasalles/srvp","path":"metrics/lpips/networks_basic.py","file_url":"https://github.com/edouardelasalles/srvp/blob/HEAD/metrics/lpips/networks_basic.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"29e5e72bcd006dcd","mcp_get_code":{"code_sha256":"29e5e72bcd006dcd"}},{"arxiv_id":"1912.04958","paper":"/paper/analyzing-and-improving-the-image-quality-of","title":"Analyzing and Improving the Image Quality of StyleGAN","date":"2019-12-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alekseynp/stylegan2-pytorch","path":"lpips/networks_basic.py","file_url":"https://github.com/alekseynp/stylegan2-pytorch/blob/HEAD/lpips/networks_basic.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"29e5e72bcd006dcd","mcp_get_code":{"code_sha256":"29e5e72bcd006dcd"}},{"arxiv_id":"1801.03924","paper":"/paper/the-unreasonable-effectiveness-of-deep","title":"The Unreasonable Effectiveness of Deep Features as a Perceptual Metric","date":"2018-01-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"richzhang/PerceptualSimilarity","path":"lpips/lpips.py","file_url":"https://github.com/richzhang/PerceptualSimilarity/blob/HEAD/lpips/lpips.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"29e5e72bcd006dcd","mcp_get_code":{"code_sha256":"29e5e72bcd006dcd"}},{"arxiv_id":"Xu_Versatile_Diffusion_Text_Images_and_Variations_All_in_One_Diffusion_ICCV_2023_paper","paper":null,"title":"arXiv:Xu_Versatile_Diffusion_Text_Images_and_Variations_All_in_One_Diffusion_ICCV_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"SHI-Labs/Versatile-Diffusion","path":"lib/model_zoo/autokl_utils.py","file_url":"https://github.com/SHI-Labs/Versatile-Diffusion/blob/HEAD/lib/model_zoo/autokl_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e268053216b1dd62","mcp_get_code":{"code_sha256":"e268053216b1dd62"}},{"arxiv_id":"Park_Perception-Oriented_Single_Image_Super-Resolution_Using_Optimal_Objective_Estimation_CVPR_2023_paper","paper":null,"title":"arXiv:Park_Perception-Oriented_Single_Image_Super-Resolution_Using_Optimal_Objective_Estimation_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"seungho-snu/SROOE","path":"codes/PerceptualSimilarity/models/networks_basic.py","file_url":"https://github.com/seungho-snu/SROOE/blob/HEAD/codes/PerceptualSimilarity/models/networks_basic.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"29e5e72bcd006dcd","mcp_get_code":{"code_sha256":"29e5e72bcd006dcd"}},{"arxiv_id":"Ni_CHAIN_Enhancing_Generalization_in_Data-Efficient_GANs_via_lipsCHitz_continuity_constrAIned_CVPR_2024_paper","paper":null,"title":"arXiv:Ni_CHAIN_Enhancing_Generalization_in_Data-Efficient_GANs_via_lipsCHitz_continuity_constrAIned_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"MaxwellYaoNi/CHAIN","path":"FastGANDBig/lpips/networks_basic.py","file_url":"https://github.com/MaxwellYaoNi/CHAIN/blob/HEAD/FastGANDBig/lpips/networks_basic.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"29e5e72bcd006dcd","mcp_get_code":{"code_sha256":"29e5e72bcd006dcd"}}]}