{"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/off-diagonal","entry":"off_diagonal","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":22,"n_papers_ran":21,"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":5,"n_samples_ran":3,"n_samples_fingerprinted":2,"n_places":23,"n_places_pointer_only":8,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":2,"ran":1,"unverified":2},"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":"2609.01427","paper":"/paper/arxiv-2609-01427","title":"Pix2Rep-v2: Data-Efficient Representation Learning for Dense Medical Imaging Applications","date":null,"month_inferred_from_arxiv_id":"2026-09","title_source":"syntology","repo":"BioMedTP/pix2rep-v2","path":"pix2repv2/utils/losses.py","file_url":"https://github.com/BioMedTP/pix2rep-v2/blob/HEAD/pix2repv2/utils/losses.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"3e30d88eaef01190","mcp_get_code":{"code_sha256":"3e30d88eaef01190"}},{"arxiv_id":"2506.02612","paper":"/paper/simple-good-fast-self-supervised-world-models","title":"Simple, Good, Fast: Self-Supervised World Models Free of Baggage","date":"2025-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jrobine/sgf","path":"src/wm.py","file_url":"https://github.com/jrobine/sgf/blob/HEAD/src/wm.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cbcbe66ba5258fb1","mcp_get_code":{"code_sha256":"cbcbe66ba5258fb1"}},{"arxiv_id":"2505.17589","paper":"/paper/cosyvoice-3-towards-in-the-wild-speech","title":"CosyVoice 3: Towards In-the-wild Speech Generation via Scaling-up and Post-training","date":"2025-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"funaudiollm/cv3-eval","path":"utils/3D-Speaker/speakerlab/loss/dino_loss.py","file_url":"https://github.com/funaudiollm/cv3-eval/blob/HEAD/utils/3D-Speaker/speakerlab/loss/dino_loss.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"3e30d88eaef01190","mcp_get_code":{"code_sha256":"3e30d88eaef01190"}},{"arxiv_id":"2406.16231","paper":"/paper/gradual-divergence-for-seamless-adaptation-a","title":"Gradual Divergence for Seamless Adaptation: A Novel Domain Incremental Learning Method","date":"2024-06-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"3e30d88eaef01190","mcp_get_code":{"code_sha256":"3e30d88eaef01190"}},{"arxiv_id":"2404.04575","paper":"/paper/to-cool-or-not-to-cool-temperature-network","title":"To Cool or not to Cool? Temperature Network Meets Large Foundation Models via DRO","date":"2024-04-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhqiu/tempnet","path":"Bimodal_CL/models/losses.py","file_url":"https://github.com/zhqiu/tempnet/blob/HEAD/Bimodal_CL/models/losses.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3e30d88eaef01190","mcp_get_code":{"code_sha256":"3e30d88eaef01190"}},{"arxiv_id":"2401.15316","paper":"/paper/unsee-unsupervised-non-contrastive-sentence","title":"UNSEE: Unsupervised Non-contrastive Sentence Embeddings","date":"2024-01-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"asparius/unsee","path":"sentence_transformers/losses/BYOLoss.py","file_url":"https://github.com/asparius/unsee/blob/HEAD/sentence_transformers/losses/BYOLoss.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"3e30d88eaef01190","mcp_get_code":{"code_sha256":"3e30d88eaef01190"}},{"arxiv_id":"2401.03145","paper":"/paper/self-supervised-feature-adaptation-for-3d","title":"Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection","date":"2024-01-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yuanpengtu/LSFA","path":"Adaptation/fusion_pretrain.py","file_url":"https://github.com/yuanpengtu/LSFA/blob/HEAD/Adaptation/fusion_pretrain.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3e30d88eaef01190","mcp_get_code":{"code_sha256":"3e30d88eaef01190"}},{"arxiv_id":"2311.16098","paper":"/paper/on-bringing-robots-home","title":"On Bringing Robots Home","date":"2023-11-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"notmahi/dobb-e","path":"stick-data-collection/utils/models.py","file_url":"https://github.com/notmahi/dobb-e/blob/HEAD/stick-data-collection/utils/models.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3f8d11b7dad3ba76","mcp_get_code":{"code_sha256":"3f8d11b7dad3ba76"}},{"arxiv_id":"2309.05300","paper":"/paper/decur-decoupling-common-unique","title":"Decoupling Common and Unique Representations for Multimodal Self-supervised Learning","date":"2023-09-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhu-xlab/decur","path":"src/pretrain/models/decur.py","file_url":"https://github.com/zhu-xlab/decur/blob/HEAD/src/pretrain/models/decur.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"3e30d88eaef01190","mcp_get_code":{"code_sha256":"3e30d88eaef01190"}},{"arxiv_id":"2308.01698","paper":"/paper/balanced-destruction-reconstruction-dynamics","title":"Balanced Destruction-Reconstruction Dynamics for Memory-replay Class Incremental Learning","date":"2023-08-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zyuh/bdr-main","path":"approach/aux_loss.py","file_url":"https://github.com/zyuh/bdr-main/blob/HEAD/approach/aux_loss.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3e30d88eaef01190","mcp_get_code":{"code_sha256":"3e30d88eaef01190"}},{"arxiv_id":"2304.13850","paper":"/paper/do-ssl-models-have-deja-vu-a-case-of-1","title":"Do SSL Models Have Déjà Vu? A Case of Unintended Memorization in Self-supervised Learning","date":"2023-04-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/dejavu","path":"train_SSL.py","file_url":"https://github.com/facebookresearch/dejavu/blob/HEAD/train_SSL.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3e30d88eaef01190","mcp_get_code":{"code_sha256":"3e30d88eaef01190"}},{"arxiv_id":"2303.01986","paper":"/paper/towards-democratizing-joint-embedding-self","title":"Towards Democratizing Joint-Embedding Self-Supervised Learning","date":"2023-03-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"3e30d88eaef01190","mcp_get_code":{"code_sha256":"3e30d88eaef01190"}},{"arxiv_id":"2302.14138","paper":"/paper/layer-grafted-pre-training-bridging","title":"Layer Grafted Pre-training: Bridging Contrastive Learning And Masked Image Modeling For Label-Efficient Representations","date":"2023-02-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"3e30d88eaef01190","mcp_get_code":{"code_sha256":"3e30d88eaef01190"}},{"arxiv_id":"2302.11346","paper":"/paper/task-aware-information-routing-from-common","title":"Task-Aware Information Routing from Common Representation Space in Lifelong Learning","date":"2023-02-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"neurai-lab/tamil","path":"models/tam.py","file_url":"https://github.com/neurai-lab/tamil/blob/HEAD/models/tam.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3e30d88eaef01190","mcp_get_code":{"code_sha256":"3e30d88eaef01190"}},{"arxiv_id":"2211.10831","paper":"/paper/joint-embedding-predictive-architectures","title":"Joint Embedding Predictive Architectures Focus on Slow Features","date":"2022-11-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vladisai/jepa_ssl_neurips_2022","path":"vicreg.py","file_url":"https://github.com/vladisai/jepa_ssl_neurips_2022/blob/HEAD/vicreg.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3e30d88eaef01190","mcp_get_code":{"code_sha256":"3e30d88eaef01190"}},{"arxiv_id":"2211.10831","paper":"/paper/joint-embedding-predictive-architectures","title":"Joint Embedding Predictive Architectures Focus on Slow Features","date":"2022-11-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vladisai/jepa_ssl_neurips_2022","path":"diagnostics.py","file_url":"https://github.com/vladisai/jepa_ssl_neurips_2022/blob/HEAD/diagnostics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"22c840277e806c06","mcp_get_code":{"code_sha256":"22c840277e806c06"}},{"arxiv_id":"2207.04998","paper":"/paper/consistency-is-the-key-to-further-mitigating","title":"Consistency is the key to further mitigating catastrophic forgetting in continual learning","date":"2022-07-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"neurai-lab/consistencycl","path":"models/cr.py","file_url":"https://github.com/neurai-lab/consistencycl/blob/HEAD/models/cr.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3e30d88eaef01190","mcp_get_code":{"code_sha256":"3e30d88eaef01190"}},{"arxiv_id":"2112.04731","paper":"/paper/mimicking-the-oracle-an-initial-phase","title":"Mimicking the Oracle: An Initial Phase Decorrelation Approach for Class Incremental Learning","date":"2021-12-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yujun-shi/cwd","path":"src/approach/aux_loss.py","file_url":"https://github.com/yujun-shi/cwd/blob/HEAD/src/approach/aux_loss.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3e30d88eaef01190","mcp_get_code":{"code_sha256":"3e30d88eaef01190"}},{"arxiv_id":"2105.04906","paper":"/paper/vicreg-variance-invariance-covariance","title":"VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning","date":"2021-05-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AnnaManasyan/VICReg","path":"loss.py","file_url":"https://github.com/AnnaManasyan/VICReg/blob/HEAD/loss.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3e30d88eaef01190","mcp_get_code":{"code_sha256":"3e30d88eaef01190"}},{"arxiv_id":"2105.00470","paper":"/paper/on-feature-decorrelation-in-self-supervised","title":"On Feature Decorrelation in Self-Supervised Learning","date":"2021-05-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PatrickHua/FeatureDecorrelationSSL","path":"models/barlowtwins.py","file_url":"https://github.com/PatrickHua/FeatureDecorrelationSSL/blob/HEAD/models/barlowtwins.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fcaf2040c9a25a15","mcp_get_code":{"code_sha256":"fcaf2040c9a25a15"}},{"arxiv_id":"2007.16189","paper":"/paper/self-supervised-learning-through-the-eyes-of","title":"Self-supervised learning through the eyes of a child","date":"2020-07-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"agentic-learning-ai-lab/memory-storyboard","path":"osiris_model.py","file_url":"https://github.com/agentic-learning-ai-lab/memory-storyboard/blob/HEAD/osiris_model.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3e30d88eaef01190","mcp_get_code":{"code_sha256":"3e30d88eaef01190"}},{"arxiv_id":"2004.04136","paper":"/paper/curl-contrastive-unsupervised-representations","title":"CURL: Contrastive Unsupervised Representations for Reinforcement Learning","date":"2020-04-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"asparius/barlowrl","path":"agent.py","file_url":"https://github.com/asparius/barlowrl/blob/HEAD/agent.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3e30d88eaef01190","mcp_get_code":{"code_sha256":"3e30d88eaef01190"}},{"arxiv_id":"Shi_Mimicking_the_Oracle_An_Initial_Phase_Decorrelation_Approach_for_Class_CVPR_2022_paper","paper":null,"title":"arXiv:Shi_Mimicking_the_Oracle_An_Initial_Phase_Decorrelation_Approach_for_Class_CVPR_2022_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Yujun-Shi/CwD","path":"src/approach/aux_loss.py","file_url":"https://github.com/Yujun-Shi/CwD/blob/HEAD/src/approach/aux_loss.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3e30d88eaef01190","mcp_get_code":{"code_sha256":"3e30d88eaef01190"}}]}