{"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/densenet-cifar","entry":"densenet_cifar","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":6,"n_papers_ran":5,"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":3,"n_samples_ran":2,"n_samples_fingerprinted":0,"n_places":6,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":2,"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":"2404.03233","paper":"/paper/learn-what-you-want-to-unlearn-unlearning","title":"Learn What You Want to Unlearn: Unlearning Inversion Attacks against Machine Unlearning","date":null,"month_inferred_from_arxiv_id":"2024-04","title_source":"archive","repo":"tasi-lab/unlearning-inversion-attacks","path":"recovery/nn/densenet.py","file_url":"https://github.com/tasi-lab/unlearning-inversion-attacks/blob/HEAD/recovery/nn/densenet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7db01b64f42f28ee","mcp_get_code":{"code_sha256":"7db01b64f42f28ee"}},{"arxiv_id":"2403.07968","paper":"/paper/do-deep-neural-network-solutions-form-a-star","title":"Do Deep Neural Network Solutions Form a Star Domain?","date":"2024-03-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aktsonthalia/starlight","path":"models/densenet.py","file_url":"https://github.com/aktsonthalia/starlight/blob/HEAD/models/densenet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c8dfc9c9f2013847","mcp_get_code":{"code_sha256":"c8dfc9c9f2013847"}},{"arxiv_id":"2309.13016","paper":"/paper/understanding-deep-gradient-leakage-via-1","title":"Understanding Deep Gradient Leakage via Inversion Influence Functions","date":"2023-09-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"illidanlab/inversion-influence-function","path":"inversefed/nn/densenet.py","file_url":"https://github.com/illidanlab/inversion-influence-function/blob/HEAD/inversefed/nn/densenet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7db01b64f42f28ee","mcp_get_code":{"code_sha256":"7db01b64f42f28ee"}},{"arxiv_id":"2308.04699","paper":"/paper/gifd-a-generative-gradient-inversion-method","title":"GIFD: A Generative Gradient Inversion Method with Feature Domain Optimization","date":"2023-08-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ffhibnese/GIFD","path":"inversefed/nn/densenet.py","file_url":"https://github.com/ffhibnese/GIFD/blob/HEAD/inversefed/nn/densenet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7db01b64f42f28ee","mcp_get_code":{"code_sha256":"7db01b64f42f28ee"}},{"arxiv_id":"2107.01154","paper":"/paper/gradient-leakage-resilient-federated-learning","title":"Gradient-Leakage Resilient Federated Learning","date":"2021-07-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"KaiyuanZh/censor","path":"inversefed/nn/densenet.py","file_url":"https://github.com/KaiyuanZh/censor/blob/HEAD/inversefed/nn/densenet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7db01b64f42f28ee","mcp_get_code":{"code_sha256":"7db01b64f42f28ee"}},{"arxiv_id":"Zhang_Generative_Gradient_Inversion_via_Over-Parameterized_Networks_in_Federated_Learning_ICCV_2023_paper","paper":null,"title":"arXiv:Zhang_Generative_Gradient_Inversion_via_Over-Parameterized_Networks_in_Federated_Learning_ICCV_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"czhang024/CI-Net","path":"model/model_architectures.py","file_url":"https://github.com/czhang024/CI-Net/blob/HEAD/model/model_architectures.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cdd1a381a2c64508","mcp_get_code":{"code_sha256":"cdd1a381a2c64508"}}]}