{"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/set-gpu","entry":"set_gpu","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":12,"n_papers_ran":9,"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":0,"n_places":12,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":2,"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":"2406.18516","paper":"/paper/denoising-as-adaptation-noise-space-domain","title":"Denoising as Adaptation: Noise-Space Domain Adaptation for Image Restoration","date":"2024-06-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kangliao929/noise-da","path":"core/util.py","file_url":"https://github.com/kangliao929/noise-da/blob/HEAD/core/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"24ef4b46c563b220","mcp_get_code":{"code_sha256":"24ef4b46c563b220"}},{"arxiv_id":"2405.17022","paper":"/paper/compositional-few-shot-class-incremental","title":"Compositional Few-Shot Class-Incremental Learning","date":"2024-05-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zoilsen/comp-fscil","path":"utils.py","file_url":"https://github.com/zoilsen/comp-fscil/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8327880f0b76f9db","mcp_get_code":{"code_sha256":"8327880f0b76f9db"}},{"arxiv_id":"2404.02117","paper":"/paper/pre-trained-vision-and-language-transformers","title":"Pre-trained Vision and Language Transformers Are Few-Shot Incremental Learners","date":"2024-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"KHU-AGI/PriViLege","path":"utils.py","file_url":"https://github.com/KHU-AGI/PriViLege/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8327880f0b76f9db","mcp_get_code":{"code_sha256":"8327880f0b76f9db"}},{"arxiv_id":"2310.07492","paper":"/paper/boosting-black-box-attack-to-deep-neural","title":"Boosting Black-box Attack to Deep Neural Networks with Conditional Diffusion Models","date":"2023-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ryliu68/CDMA","path":"core/util.py","file_url":"https://github.com/ryliu68/CDMA/blob/HEAD/core/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"24ef4b46c563b220","mcp_get_code":{"code_sha256":"24ef4b46c563b220"}},{"arxiv_id":"2305.16948","paper":"/paper/meta-prediction-model-for-distillation-aware","title":"Meta-prediction Model for Distillation-Aware NAS on Unseen Datasets","date":"2023-05-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"CownowAn/DaSS","path":"meta_train.py","file_url":"https://github.com/CownowAn/DaSS/blob/HEAD/meta_train.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9da65a108aaf0a7c","mcp_get_code":{"code_sha256":"9da65a108aaf0a7c"}},{"arxiv_id":"2210.04524","paper":"/paper/margin-based-few-shot-class-incremental","title":"Margin-Based Few-Shot Class-Incremental Learning with Class-Level Overfitting Mitigation","date":"2022-10-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zoilsen/clom","path":"utils.py","file_url":"https://github.com/zoilsen/clom/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8327880f0b76f9db","mcp_get_code":{"code_sha256":"8327880f0b76f9db"}},{"arxiv_id":"2203.14145","paper":"/paper/reverse-engineering-of-imperceptible-1","title":"Reverse Engineering of Imperceptible Adversarial Image Perturbations","date":"2022-03-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yifanfanfanfan/reverse-engineering-of-imperceptible-adversarial-image-perturbations","path":"RED_train_with_trans.py","file_url":"https://github.com/yifanfanfanfan/reverse-engineering-of-imperceptible-adversarial-image-perturbations/blob/HEAD/RED_train_with_trans.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6a19bfad60fac463","mcp_get_code":{"code_sha256":"6a19bfad60fac463"}},{"arxiv_id":"2110.14068","paper":"/paper/drawing-robust-scratch-tickets-subnetworks","title":"Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks","date":"2021-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RICE-EIC/Robust-Scratch-Ticket","path":"train_full.py","file_url":"https://github.com/RICE-EIC/Robust-Scratch-Ticket/blob/HEAD/train_full.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9fd69d0fcf184abd","mcp_get_code":{"code_sha256":"9fd69d0fcf184abd"}},{"arxiv_id":"2104.03047","paper":"/paper/few-shot-incremental-learning-with","title":"Few-Shot Incremental Learning with Continually Evolved Classifiers","date":"2021-04-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"icoz69/cec-cvpr2021","path":"utils.py","file_url":"https://github.com/icoz69/cec-cvpr2021/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8327880f0b76f9db","mcp_get_code":{"code_sha256":"8327880f0b76f9db"}},{"arxiv_id":"2003.06777","paper":"/paper/deepemd-few-shot-image-classification-with","title":"DeepEMD: Differentiable Earth Mover's Distance for Few-Shot Learning","date":"2020-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"icoz69/DeepEMD","path":"Models/utils.py","file_url":"https://github.com/icoz69/DeepEMD/blob/HEAD/Models/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8327880f0b76f9db","mcp_get_code":{"code_sha256":"8327880f0b76f9db"}},{"arxiv_id":"1904.08479","paper":"/paper/lcc-learning-to-customize-and-combine-neural","title":"An Ensemble of Epoch-wise Empirical Bayes for Few-shot Learning","date":"2019-04-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yaoyao-liu/E3BM","path":"utils/gpu_tools.py","file_url":"https://github.com/yaoyao-liu/E3BM/blob/HEAD/utils/gpu_tools.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":"d323924483f46bce","mcp_get_code":{"code_sha256":"d323924483f46bce"}},{"arxiv_id":"aaai_28199","paper":null,"title":"arXiv:aaai_28199","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"YoferChen/FedST","path":"core/util.py","file_url":"https://github.com/YoferChen/FedST/blob/HEAD/core/util.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":"24ef4b46c563b220","mcp_get_code":{"code_sha256":"24ef4b46c563b220"}}]}