{"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/upsampleblock","entry":"UpsampleBlock","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":9,"n_papers_ran":6,"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":6,"n_samples_fingerprinted":4,"n_places":9,"n_places_pointer_only":4,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":5,"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":"2603.04430","paper":"/paper/arxiv-2603-04430","title":"Flowers: A Warp Drive for Neural PDE Solvers","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"t-muser/flowers","path":"flowers/models/flower.py","file_url":"https://github.com/t-muser/flowers/blob/HEAD/flowers/models/flower.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"b34136038ae8642b","mcp_get_code":{"code_sha256":"b34136038ae8642b"}},{"arxiv_id":"2601.17657","paper":"/paper/arxiv-2601-17657","title":"SPACE-CLIP: Spatial Perception via Adaptive CLIP Embeddings for Monocular Depth Estimation","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"taewan2002/space-clip","path":"space_clip.py","file_url":"https://github.com/taewan2002/space-clip/blob/HEAD/space_clip.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":"8df912190eb94c8d","mcp_get_code":{"code_sha256":"8df912190eb94c8d"}},{"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/Gridnet.py","file_url":"https://github.com/NYCU-MAPL/MHLVC/blob/HEAD/compressai/RIFE/Gridnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1ea9c7f887bd0270","mcp_get_code":{"code_sha256":"1ea9c7f887bd0270"}},{"arxiv_id":"2403.05419","paper":"/paper/rethinking-transformers-pre-training-for","title":"Rethinking Transformers Pre-training for Multi-Spectral Satellite Imagery","date":"2024-03-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"techmn/satmae_pp","path":"models_mae_group_channels.py","file_url":"https://github.com/techmn/satmae_pp/blob/HEAD/models_mae_group_channels.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"79d85f1a4a3e06f9","mcp_get_code":{"code_sha256":"79d85f1a4a3e06f9"}},{"arxiv_id":"2203.10812","paper":"/paper/arm-any-time-super-resolution-method","title":"ARM: Any-Time Super-Resolution Method","date":"2022-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenbong/ARM-Net","path":"models/archs/CARN_arch.py","file_url":"https://github.com/chenbong/ARM-Net/blob/HEAD/models/archs/CARN_arch.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"453e4502f95ba8e4","mcp_get_code":{"code_sha256":"453e4502f95ba8e4"}},{"arxiv_id":"2104.06403","paper":"/paper/lite-hrnet-a-lightweight-high-resolution","title":"Lite-HRNet: A Lightweight High-Resolution Network","date":"2021-04-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zh320/realtime-semantic-segmentation-pytorch","path":"models/lite_hrnet.py","file_url":"https://github.com/zh320/realtime-semantic-segmentation-pytorch/blob/HEAD/models/lite_hrnet.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":"21e87966c949f4a8","mcp_get_code":{"code_sha256":"21e87966c949f4a8"}},{"arxiv_id":"2103.04039","paper":"/paper/classsr-a-general-framework-to-accelerate","title":"ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic","date":"2021-03-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Xiangtaokong/ClassSR","path":"codes/models/archs/classSR_carn_arch.py","file_url":"https://github.com/Xiangtaokong/ClassSR/blob/HEAD/codes/models/archs/classSR_carn_arch.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f02bd3c8d9853c45","mcp_get_code":{"code_sha256":"f02bd3c8d9853c45"}},{"arxiv_id":"2101.06046","paper":"/paper/counterfactual-generative-networks-1","title":"Counterfactual Generative Networks","date":"2021-01-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"autonomousvision/counterfactual_generative_networks","path":"mnists/models/cgn.py","file_url":"https://github.com/autonomousvision/counterfactual_generative_networks/blob/HEAD/mnists/models/cgn.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7952987e7786816f","mcp_get_code":{"code_sha256":"7952987e7786816f"}},{"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":"zh320/medical-segmentation-pytorch","path":"models/unet.py","file_url":"https://github.com/zh320/medical-segmentation-pytorch/blob/HEAD/models/unet.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":"3db5e591c6105d95","mcp_get_code":{"code_sha256":"3db5e591c6105d95"}}]}