{"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/total-variation","entry":"total_variation","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":14,"n_papers_ran":1,"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":1,"n_samples_fingerprinted":1,"n_places":14,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":1,"ran":0,"unverified":8},"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":"2609.04166","paper":"/paper/arxiv-2609-04166","title":"From Deceptive Outputs to Deceptive Mechanisms: A Causal Framework for Language-Model Deception Research","date":null,"month_inferred_from_arxiv_id":"2026-09","title_source":"syntology","repo":"yshk-mxim/deceptive-mechanism","path":"replication_package/experiment1/analysis/src/deceit_analysis/metrics.py","file_url":"https://github.com/yshk-mxim/deceptive-mechanism/blob/HEAD/replication_package/experiment1/analysis/src/deceit_analysis/metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5a236a9c5479d07f","mcp_get_code":{"code_sha256":"5a236a9c5479d07f"}},{"arxiv_id":"2603.17623","paper":"/paper/arxiv-2603-17623","title":"ARES: Scalable and Practical Gradient Inversion Attack in Federated Learning through Activation Recovery","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"gaow0007/ATSPrivacy","path":"inversefed/metrics.py","file_url":"https://github.com/gaow0007/ATSPrivacy/blob/HEAD/inversefed/metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3d23162c75564e01","mcp_get_code":{"code_sha256":"3d23162c75564e01"}},{"arxiv_id":"2410.17760","paper":"/paper/topology-meets-machine-learning-an","title":"Topology meets Machine Learning: An Introduction using the Euler Characteristic Transform","date":"2024-10-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aidos-lab/ECT","path":"ect_image.py","file_url":"https://github.com/aidos-lab/ECT/blob/HEAD/ect_image.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"1c472c25567e467e","mcp_get_code":{"code_sha256":"1c472c25567e467e"}},{"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/metrics.py","file_url":"https://github.com/tasi-lab/unlearning-inversion-attacks/blob/HEAD/recovery/metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3d23162c75564e01","mcp_get_code":{"code_sha256":"3d23162c75564e01"}},{"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/metrics.py","file_url":"https://github.com/illidanlab/inversion-influence-function/blob/HEAD/inversefed/metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3d23162c75564e01","mcp_get_code":{"code_sha256":"3d23162c75564e01"}},{"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/metrics.py","file_url":"https://github.com/ffhibnese/GIFD/blob/HEAD/inversefed/metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3d23162c75564e01","mcp_get_code":{"code_sha256":"3d23162c75564e01"}},{"arxiv_id":"2306.00127","paper":"/paper/surrogate-model-extension-sme-a-fast-and","title":"Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning","date":"2023-05-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"junyizhu-ai/surrogate_model_extension","path":"sme/Adversary/adversary.py","file_url":"https://github.com/junyizhu-ai/surrogate_model_extension/blob/HEAD/sme/Adversary/adversary.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"751c3b578dca1309","mcp_get_code":{"code_sha256":"751c3b578dca1309"}},{"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/metrics.py","file_url":"https://github.com/KaiyuanZh/censor/blob/HEAD/inversefed/metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3d23162c75564e01","mcp_get_code":{"code_sha256":"3d23162c75564e01"}},{"arxiv_id":"1910.02190","paper":"/paper/kornia-an-open-source-differentiable-computer","title":"Kornia: an Open Source Differentiable Computer Vision Library for PyTorch","date":"2019-10-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"manyids2/kornia","path":"kornia/losses/total_variation.py","file_url":"https://github.com/manyids2/kornia/blob/HEAD/kornia/losses/total_variation.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":"ace65b30a9175199","mcp_get_code":{"code_sha256":"ace65b30a9175199"}},{"arxiv_id":"1907.06679","paper":"/paper/towards-near-imperceptible-steganographic","title":"Towards Near-imperceptible Steganographic Text","date":"2019-07-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"falcondai/lm-steganography","path":"bucket.py","file_url":"https://github.com/falcondai/lm-steganography/blob/HEAD/bucket.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5c50495d583afb43","mcp_get_code":{"code_sha256":"5c50495d583afb43"}},{"arxiv_id":"1806.07421","paper":"/paper/rise-randomized-input-sampling-for","title":"RISE: Randomized Input Sampling for Explanation of Black-box Models","date":"2018-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vlue-c/PyTorch-Explanations","path":"torchvex/meaningful_perturbation/mask.py","file_url":"https://github.com/vlue-c/PyTorch-Explanations/blob/HEAD/torchvex/meaningful_perturbation/mask.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b0396293b38da996","mcp_get_code":{"code_sha256":"b0396293b38da996"}},{"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":"utils/reconstructed.py","file_url":"https://github.com/czhang024/CI-Net/blob/HEAD/utils/reconstructed.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4131f97dcfa84ceb","mcp_get_code":{"code_sha256":"4131f97dcfa84ceb"}},{"arxiv_id":"Sun_Soteria_Provable_Defense_Against_Privacy_Leakage_in_Federated_Learning_From_CVPR_2021_paper","paper":null,"title":"arXiv:Sun_Soteria_Provable_Defense_Against_Privacy_Leakage_in_Federated_Learning_From_CVPR_2021_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"jeremy313/Soteria","path":"GS_attack/inversefed/metrics.py","file_url":"https://github.com/jeremy313/Soteria/blob/HEAD/GS_attack/inversefed/metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3d23162c75564e01","mcp_get_code":{"code_sha256":"3d23162c75564e01"}},{"arxiv_id":"Gan_GaussianOcc_Fully_Self-supervised_and_Efficient_3D_Occupancy_Estimation_with_Gaussian_ICCV_2025_paper","paper":null,"title":"arXiv:Gan_GaussianOcc_Fully_Self-supervised_and_Efficient_3D_Occupancy_Estimation_with_Gaussian_ICCV_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"GANWANSHUI/GaussianOcc","path":"networks/occupancy_decoder.py","file_url":"https://github.com/GANWANSHUI/GaussianOcc/blob/HEAD/networks/occupancy_decoder.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":"9951c8a5bcd02850","mcp_get_code":{"code_sha256":"9951c8a5bcd02850"}}]}