{"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/patchunembed","entry":"PatchUnEmbed","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":15,"n_papers_ran":13,"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":18,"n_samples_ran":16,"n_samples_fingerprinted":0,"n_places":18,"n_places_pointer_only":8,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":16,"unverified":2},"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":"2602.00490","paper":"/paper/arxiv-2602-00490","title":"HSSDCT: Factorized Spatial-Spectral Correlation for Hyperspectral Image Fusion","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"jemmyleee/HSSDCT","path":"models/hssdct.py","file_url":"https://github.com/jemmyleee/HSSDCT/blob/HEAD/models/hssdct.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"68bc15685467eed9","mcp_get_code":{"code_sha256":"68bc15685467eed9"}},{"arxiv_id":"2601.01406","paper":"/paper/arxiv-2601-01406","title":"SwinIFS: Landmark-Guided Swin Transformer For Identity-Preserving Face Super-Resolution","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"Habiba123-stack/SwinIFS","path":"models/network_swinfsr.py","file_url":"https://github.com/Habiba123-stack/SwinIFS/blob/HEAD/models/network_swinfsr.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"59d0c38b72d7a542","mcp_get_code":{"code_sha256":"59d0c38b72d7a542"}},{"arxiv_id":"2503.18446","paper":"/paper/latent-space-super-resolution-for-higher","title":"Latent Space Super-Resolution for Higher-Resolution Image Generation with Diffusion Models","date":"2025-03-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"3587jjh/lsrna","path":"lsr/swinir.py","file_url":"https://github.com/3587jjh/lsrna/blob/HEAD/lsr/swinir.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"223f9228b44f77f4","mcp_get_code":{"code_sha256":"223f9228b44f77f4"}},{"arxiv_id":"2503.13147","paper":"/paper/iterative-predictor-critic-code-decoding-for","title":"Iterative Predictor-Critic Code Decoding for Real-World Image Dehazing","date":"2025-03-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Jiayi-Fu/IPC-Dehaze","path":"basicsr/archs/dehazeToken_arch.py","file_url":"https://github.com/Jiayi-Fu/IPC-Dehaze/blob/HEAD/basicsr/archs/dehazeToken_arch.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"7c8f22b18444dd18","mcp_get_code":{"code_sha256":"7c8f22b18444dd18"}},{"arxiv_id":"2502.19854","paper":"/paper/one-model-for-all-low-level-task-interaction","title":"One Model for ALL: Low-Level Task Interaction Is a Key to Task-Agnostic Image Fusion","date":"2025-02-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AWCXV/GIFNet","path":"GIFNet_model.py","file_url":"https://github.com/AWCXV/GIFNet/blob/HEAD/GIFNet_model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1a21d2e8622fc968","mcp_get_code":{"code_sha256":"1a21d2e8622fc968"}},{"arxiv_id":"2401.08209","paper":"/paper/transcending-the-limit-of-local-window","title":"Transcending the Limit of Local Window: Advanced Super-Resolution Transformer with Adaptive Token Dictionary","date":"2024-01-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"labshuhanggu/adaptive-token-dictionary","path":"basicsr/archs/atd_arch.py","file_url":"https://github.com/labshuhanggu/adaptive-token-dictionary/blob/HEAD/basicsr/archs/atd_arch.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":"3732cc4a6f0ef56f","mcp_get_code":{"code_sha256":"3732cc4a6f0ef56f"}},{"arxiv_id":"2401.00766","paper":"/paper/bracketing-is-all-you-need-unifying-image","title":"Exposure Bracketing Is All You Need For A High-Quality Image","date":"2024-01-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cszhilu1998/selfhdr","path":"models/hdr_transformer.py","file_url":"https://github.com/cszhilu1998/selfhdr/blob/HEAD/models/hdr_transformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"8a1184e54458a8ef","mcp_get_code":{"code_sha256":"8a1184e54458a8ef"}},{"arxiv_id":"2307.14010","paper":"/paper/essaformer-efficient-transformer-for","title":"ESSAformer: Efficient Transformer for Hyperspectral Image Super-resolution","date":"2023-07-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rexzhan/essaformer","path":"ESSA.py","file_url":"https://github.com/rexzhan/essaformer/blob/HEAD/ESSA.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Unlicense","inline_ok":true,"code_sha256_prefix":"52a009960f8e40d1","mcp_get_code":{"code_sha256":"52a009960f8e40d1"}},{"arxiv_id":"2304.03994","paper":"/paper/ridcp-revitalizing-real-image-dehazing-via","title":"RIDCP: Revitalizing Real Image Dehazing via High-Quality Codebook Priors","date":"2023-04-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RQ-Wu/RIDCP_dehazing","path":"basicsr/archs/dehaze_vq_weight_arch.py","file_url":"https://github.com/RQ-Wu/RIDCP_dehazing/blob/HEAD/basicsr/archs/dehaze_vq_weight_arch.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"57e191838eccb314","mcp_get_code":{"code_sha256":"57e191838eccb314"}},{"arxiv_id":"2302.10414","paper":"/paper/improving-scene-text-image-super-resolution","title":"Improving Scene Text Image Super-resolution via Dual Prior Modulation Network","date":"2023-02-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jdfxzzy/DPMN","path":"model/pgrm.py","file_url":"https://github.com/jdfxzzy/DPMN/blob/HEAD/model/pgrm.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8e37847cbc71fa7c","mcp_get_code":{"code_sha256":"8e37847cbc71fa7c"}},{"arxiv_id":"2210.01427","paper":"/paper/accurate-image-restoration-with-attention","title":"Accurate Image Restoration with Attention Retractable Transformer","date":"2022-10-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gladzhang/art","path":"basicsr/archs/art_arch.py","file_url":"https://github.com/gladzhang/art/blob/HEAD/basicsr/archs/art_arch.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":"3e54d348693d9f3a","mcp_get_code":{"code_sha256":"3e54d348693d9f3a"}},{"arxiv_id":"2203.11589","paper":"/paper/adaptive-patch-exiting-for-scalable-single","title":"Adaptive Patch Exiting for Scalable Single Image Super-Resolution","date":"2022-03-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"littlepure2333/APE","path":"model/swinir_ape.py","file_url":"https://github.com/littlepure2333/APE/blob/HEAD/model/swinir_ape.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bafcd1f59633494d","mcp_get_code":{"code_sha256":"bafcd1f59633494d"}},{"arxiv_id":"2108.10257","paper":"/paper/swinir-image-restoration-using-swin","title":"SwinIR: Image Restoration Using Swin Transformer","date":"2021-08-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pilot7747/sldl","path":"sldl/image/swinir.py","file_url":"https://github.com/pilot7747/sldl/blob/HEAD/sldl/image/swinir.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"2d3ebe5f83f401e2","mcp_get_code":{"code_sha256":"2d3ebe5f83f401e2"}},{"arxiv_id":"2108.10257","paper":"/paper/swinir-image-restoration-using-swin","title":"SwinIR: Image Restoration Using Swin Transformer","date":"2021-08-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mv-lab/swin2sr","path":"models/network_swin2sr.py","file_url":"https://github.com/mv-lab/swin2sr/blob/HEAD/models/network_swin2sr.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":"926e8d276bb28da7","mcp_get_code":{"code_sha256":"926e8d276bb28da7"}},{"arxiv_id":"2108.10257","paper":"/paper/swinir-image-restoration-using-swin","title":"SwinIR: Image Restoration Using Swin Transformer","date":"2021-08-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"XPixelGroup/BasicSR","path":"basicsr/archs/swinir_arch.py","file_url":"https://github.com/XPixelGroup/BasicSR/blob/HEAD/basicsr/archs/swinir_arch.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"1ca0dd483faa6682","mcp_get_code":{"code_sha256":"1ca0dd483faa6682"}},{"arxiv_id":"2103.14030","paper":"/paper/swin-transformer-hierarchical-vision","title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","date":"2021-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rami0205/ngramswin","path":"my_model/swinirng.py","file_url":"https://github.com/rami0205/ngramswin/blob/HEAD/my_model/swinirng.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":"eeab3665e6a12aab","mcp_get_code":{"code_sha256":"eeab3665e6a12aab"}},{"arxiv_id":"2103.14030","paper":"/paper/swin-transformer-hierarchical-vision","title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","date":"2021-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ayanglab/swinganmr","path":"models/network_swinmr.py","file_url":"https://github.com/ayanglab/swinganmr/blob/HEAD/models/network_swinmr.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"1d1fd42e3774afdb","mcp_get_code":{"code_sha256":"1d1fd42e3774afdb"}},{"arxiv_id":"2003.12039","paper":"/paper/raft-recurrent-all-pairs-field-transforms-for","title":"RAFT: Recurrent All-Pairs Field Transforms for Optical Flow","date":"2020-03-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"esakak/sevc","path":"src/models/submodels/RSTB.py","file_url":"https://github.com/esakak/sevc/blob/HEAD/src/models/submodels/RSTB.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7d0bab782fa467e4","mcp_get_code":{"code_sha256":"7d0bab782fa467e4"}}]}