{"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/get-gaussian-kernel","entry":"get_gaussian_kernel","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":17,"n_papers_ran":10,"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":11,"n_samples_ran":4,"n_samples_fingerprinted":0,"n_places":17,"n_places_pointer_only":5,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":2,"ran_fixture":0,"ran":2,"unverified":7},"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":"2605.00578","paper":"/paper/arxiv-2605-00578","title":"Federated Distillation for Whole Slide Image via Gaussian-Mixture Feature Alignment and Curriculum Integration","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"HuahuaCodes/FedHD-ICML2026","path":"FedHD/utils/swd_loss.py","file_url":"https://github.com/HuahuaCodes/FedHD-ICML2026/blob/HEAD/FedHD/utils/swd_loss.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c8ceba12b6020549","mcp_get_code":{"code_sha256":"c8ceba12b6020549"}},{"arxiv_id":"2601.22763","paper":"/paper/arxiv-2601-22763","title":"Is Task-Specific Training Necessary for Anomaly Detection?","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"longkukuhi/RAD","path":"rad_mvtec_visa_3dadam.py","file_url":"https://github.com/longkukuhi/RAD/blob/HEAD/rad_mvtec_visa_3dadam.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7e823caaaa915829","mcp_get_code":{"code_sha256":"7e823caaaa915829"}},{"arxiv_id":"2505.23068","paper":"/paper/urwkv-unified-rwkv-model-with-multi-state","title":"URWKV: Unified RWKV Model with Multi-state Perspective for Low-light Image Restoration","date":"2025-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"FZU-N/URWKV","path":"custom_utils/losses.py","file_url":"https://github.com/FZU-N/URWKV/blob/HEAD/custom_utils/losses.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":"7f494e37955dbf6d","mcp_get_code":{"code_sha256":"7f494e37955dbf6d"}},{"arxiv_id":"2407.12292","paper":"/paper/any-target-can-be-offense-adversarial-example","title":"Any Target Can be Offense: Adversarial Example Generation via Generalized Latent Infection","date":"2024-07-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VL-Group/GAKer","path":"utils/gaussian_smoothing.py","file_url":"https://github.com/VL-Group/GAKer/blob/HEAD/utils/gaussian_smoothing.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2d69878edb36c4fa","mcp_get_code":{"code_sha256":"2d69878edb36c4fa"}},{"arxiv_id":"2407.10179","paper":"/paper/clip-guided-networks-for-transferable","title":"CLIP-Guided Generative Networks for Transferable Targeted Adversarial Attacks","date":"2024-07-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ffhibnese/CGNC_Targeted_Adversarial_Attacks","path":"models/generator.py","file_url":"https://github.com/ffhibnese/CGNC_Targeted_Adversarial_Attacks/blob/HEAD/models/generator.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7c954dffd6b6e3f5","mcp_get_code":{"code_sha256":"7c954dffd6b6e3f5"}},{"arxiv_id":"2406.05531","paper":"/paper/enhancing-adversarial-transferability-via","title":"Enhancing Adversarial Transferability via Information Bottleneck Constraints","date":"2024-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"biqing-qi/enhancing-adversarial-transferability-via-information-bottleneck-constraints","path":"gaussian_smoothing.py","file_url":"https://github.com/biqing-qi/enhancing-adversarial-transferability-via-information-bottleneck-constraints/blob/HEAD/gaussian_smoothing.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2d69878edb36c4fa","mcp_get_code":{"code_sha256":"2d69878edb36c4fa"}},{"arxiv_id":"2311.01323","paper":"/paper/towards-evaluating-transfer-based-attacks","title":"Towards Evaluating Transfer-based Attacks Systematically, Practically, and Fairly","date":"2023-11-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qizhangli/TA-Bench","path":"models/condgenerators.py","file_url":"https://github.com/qizhangli/TA-Bench/blob/HEAD/models/condgenerators.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7c954dffd6b6e3f5","mcp_get_code":{"code_sha256":"7c954dffd6b6e3f5"}},{"arxiv_id":"2106.12673","paper":"/paper/conditional-deformable-image-registration","title":"Conditional Deformable Image Registration with Convolutional Neural Network","date":"2021-06-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cwmok/dirac","path":"Code/Functions.py","file_url":"https://github.com/cwmok/dirac/blob/HEAD/Code/Functions.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"55263643ea3ef4b6","mcp_get_code":{"code_sha256":"55263643ea3ef4b6"}},{"arxiv_id":"2104.01431","paper":"/paper/aggregated-contextual-transformations-for","title":"Aggregated Contextual Transformations for High-Resolution Image Inpainting","date":"2021-04-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"researchmm/AOT-GAN-for-Inpainting","path":"src/loss/common.py","file_url":"https://github.com/researchmm/AOT-GAN-for-Inpainting/blob/HEAD/src/loss/common.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":"0dcc94152add7d84","mcp_get_code":{"code_sha256":"0dcc94152add7d84"}},{"arxiv_id":"2007.15651","paper":"/paper/contrastive-learning-for-unpaired-image-to","title":"Contrastive Learning for Unpaired Image-to-Image Translation","date":"2020-07-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"XiudingCai/EnCo-pytorch","path":"util/SWD/swd_pytorch.py","file_url":"https://github.com/XiudingCai/EnCo-pytorch/blob/HEAD/util/SWD/swd_pytorch.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"c8ceba12b6020549","mcp_get_code":{"code_sha256":"c8ceba12b6020549"}},{"arxiv_id":"2007.12668","paper":"/paper/kprnet-improving-projection-based-lidar","title":"KPRNet: Improving projection-based LiDAR semantic segmentation","date":"2020-07-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DeyvidKochanov-TomTom/kprnet","path":"utils/knn.py","file_url":"https://github.com/DeyvidKochanov-TomTom/kprnet/blob/HEAD/utils/knn.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f82d80ceb7289856","mcp_get_code":{"code_sha256":"f82d80ceb7289856"}},{"arxiv_id":"2006.10738","paper":"/paper/differentiable-augmentation-for-data","title":"Differentiable Augmentation for Data-Efficient GAN Training","date":"2020-06-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"uzielroy/StyleGan_FewShot","path":"metric/swd_score.py","file_url":"https://github.com/uzielroy/StyleGan_FewShot/blob/HEAD/metric/swd_score.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c8ceba12b6020549","mcp_get_code":{"code_sha256":"c8ceba12b6020549"}},{"arxiv_id":"1905.11736","paper":"/paper/cross-domain-transferability-of-adversarial","title":"Cross-Domain Transferability of Adversarial Perturbations","date":"2019-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"muzammal-naseer/cda","path":"gaussian_smoothing.py","file_url":"https://github.com/muzammal-naseer/cda/blob/HEAD/gaussian_smoothing.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2d69878edb36c4fa","mcp_get_code":{"code_sha256":"2d69878edb36c4fa"}},{"arxiv_id":"1905.00953","paper":"/paper/omni-scale-feature-learning-for-person-re","title":"Omni-Scale Feature Learning for Person Re-Identification","date":"2019-05-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"InnovArul/vidreid_cosegmentation","path":"src/utils.py","file_url":"https://github.com/InnovArul/vidreid_cosegmentation/blob/HEAD/src/utils.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":"5691966090d9a9f2","mcp_get_code":{"code_sha256":"5691966090d9a9f2"}},{"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":"koshian2/swd-pytorch","path":"swd.py","file_url":"https://github.com/koshian2/swd-pytorch/blob/HEAD/swd.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c8ceba12b6020549","mcp_get_code":{"code_sha256":"c8ceba12b6020549"}},{"arxiv_id":"aaai_27859","paper":null,"title":"arXiv:aaai_27859","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Nemo1999/Joint-TensoRF","path":"model/kernels.py","file_url":"https://github.com/Nemo1999/Joint-TensoRF/blob/HEAD/model/kernels.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"92f32ba5bc84a53c","mcp_get_code":{"code_sha256":"92f32ba5bc84a53c"}},{"arxiv_id":"Ye_Robust_Message_Embedding_via_Attention_Flow-Based_Steganography_CVPR_2025_paper","paper":null,"title":"arXiv:Ye_Robust_Message_Embedding_via_Attention_Flow-Based_Steganography_CVPR_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"huayuan4396/RMSteg","path":"src/util/qr.py","file_url":"https://github.com/huayuan4396/RMSteg/blob/HEAD/src/util/qr.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"492ddfbf033c7012","mcp_get_code":{"code_sha256":"492ddfbf033c7012"}}]}