{"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/downconv","entry":"DownConv","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":8,"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":12,"n_samples_ran":11,"n_samples_fingerprinted":7,"n_places":12,"n_places_pointer_only":7,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":11,"unverified":1},"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":"2509.22307","paper":"/paper/arxiv-2509-22307","title":"Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"JinPLu/VeloxSeg","path":"model/VeloxSeg.py","file_url":"https://github.com/JinPLu/VeloxSeg/blob/HEAD/model/VeloxSeg.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7771f22568503062","mcp_get_code":{"code_sha256":"7771f22568503062"}},{"arxiv_id":"2304.02633","paper":"/paper/hnerv-a-hybrid-neural-representation-for","title":"HNeRV: A Hybrid Neural Representation for Videos","date":"2023-04-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"haochen-rye/hnerv","path":"model_all.py","file_url":"https://github.com/haochen-rye/hnerv/blob/HEAD/model_all.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2f0b07dfa92f0524","mcp_get_code":{"code_sha256":"2f0b07dfa92f0524"}},{"arxiv_id":"2303.09040","paper":"/paper/hybrid-spectral-denoising-transformer-with","title":"Hybrid Spectral Denoising Transformer with Guided Attention","date":"2023-03-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Zeqiang-Lai/HSDT","path":"train/model/hsdt/qrnn/arch.py","file_url":"https://github.com/Zeqiang-Lai/HSDT/blob/HEAD/train/model/hsdt/qrnn/arch.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"80b8d6eefd57cc1c","mcp_get_code":{"code_sha256":"80b8d6eefd57cc1c"}},{"arxiv_id":"2212.04005","paper":"/paper/rainunet-for-super-resolution-rain-movie","title":"RainUNet for Super-Resolution Rain Movie Prediction under Spatio-temporal Shifts","date":"2022-12-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jinyxp/weather4cast-2022","path":"models/RainUNET.py","file_url":"https://github.com/jinyxp/weather4cast-2022/blob/HEAD/models/RainUNET.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5c04b46d301f8ac2","mcp_get_code":{"code_sha256":"5c04b46d301f8ac2"}},{"arxiv_id":"2212.02952","paper":"/paper/simple-baseline-for-weather-forecasting-using","title":"Simple Baseline for Weather Forecasting Using Spatiotemporal Context Aggregation Network","date":"2022-12-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"seominseok0429/w4c22-simple-baseline-for-weather-forecasting-using-spatiotemporal-context-aggregation-network","path":"models/SIANet.py","file_url":"https://github.com/seominseok0429/w4c22-simple-baseline-for-weather-forecasting-using-spatiotemporal-context-aggregation-network/blob/HEAD/models/SIANet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"8a412b5d53828864","mcp_get_code":{"code_sha256":"8a412b5d53828864"}},{"arxiv_id":"2212.02059","paper":"/paper/region-conditioned-orthogonal-3d-u-net-for","title":"Region-Conditioned Orthogonal 3D U-Net for Weather4Cast Competition","date":"2022-12-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hyeonjeong1/22-neurips-competition-baseline","path":"models/baseline_UNET3D_bottleneck.py","file_url":"https://github.com/hyeonjeong1/22-neurips-competition-baseline/blob/HEAD/models/baseline_UNET3D_bottleneck.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fa4fb2da5402dcb0","mcp_get_code":{"code_sha256":"fa4fb2da5402dcb0"}},{"arxiv_id":"2111.02995","paper":"/paper/unsupervised-change-detection-of-extreme","title":"Unsupervised Change Detection of Extreme Events Using ML On-Board","date":"2021-11-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"spaceml-org/RaVAEn","path":"src/models/ae_vae_models/deeper_vae.py","file_url":"https://github.com/spaceml-org/RaVAEn/blob/HEAD/src/models/ae_vae_models/deeper_vae.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":"5b3f009f00c8e810","mcp_get_code":{"code_sha256":"5b3f009f00c8e810"}},{"arxiv_id":"1606.06650","paper":"/paper/3d-u-net-learning-dense-volumetric","title":"3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation","date":"2016-06-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"elektronn/elektronn3","path":"elektronn3/models/unet.py","file_url":"https://github.com/elektronn/elektronn3/blob/HEAD/elektronn3/models/unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4fbc8e441e25d534","mcp_get_code":{"code_sha256":"4fbc8e441e25d534"}},{"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":"burakalperen/Pytorch-Semantic-Segmentation","path":"UNet_Multi-class/models/model.py","file_url":"https://github.com/burakalperen/Pytorch-Semantic-Segmentation/blob/HEAD/UNet_Multi-class/models/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"482b8a65ce920ce8","mcp_get_code":{"code_sha256":"482b8a65ce920ce8"}},{"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":"shanglianlm0525/CvPytorch","path":"src/models/unet.py","file_url":"https://github.com/shanglianlm0525/CvPytorch/blob/HEAD/src/models/unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4ac0c44ff9cb1718","mcp_get_code":{"code_sha256":"4ac0c44ff9cb1718"}},{"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":"minerva-ml/open-solution-mapping-challenge","path":"src/steps/pytorch/architectures/unet.py","file_url":"https://github.com/minerva-ml/open-solution-mapping-challenge/blob/HEAD/src/steps/pytorch/architectures/unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e2a5e722ef59f30d","mcp_get_code":{"code_sha256":"e2a5e722ef59f30d"}},{"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":"neuropoly/multiclass-segmentation","path":"models.py","file_url":"https://github.com/neuropoly/multiclass-segmentation/blob/HEAD/models.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c809d564e424c607","mcp_get_code":{"code_sha256":"c809d564e424c607"}}]}