{"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/adjusted-rand-index","entry":"adjusted_rand_index","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":8,"n_papers_ran":2,"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":8,"n_samples_ran":2,"n_samples_fingerprinted":2,"n_places":8,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":1,"ran":1,"unverified":6},"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":"2603.22758","paper":"/paper/arxiv-2603-22758","title":"Reconstruction-Guided Slot Curriculum: Addressing Object Over-Fragmentation in Video Object-Centric Learning","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"wjun0830/SlotCurri","path":"slotcurri/metrics.py","file_url":"https://github.com/wjun0830/SlotCurri/blob/HEAD/slotcurri/metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"969b8234f202c416","mcp_get_code":{"code_sha256":"969b8234f202c416"}},{"arxiv_id":"2405.17283","paper":"/paper/recurrent-complex-weighted-autoencoders-for","title":"Recurrent Complex-Weighted Autoencoders for Unsupervised Object Discovery","date":"2024-05-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"agopal42/syncx","path":"utils/utils_general.py","file_url":"https://github.com/agopal42/syncx/blob/HEAD/utils/utils_general.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"305420f9f64aa31a","mcp_get_code":{"code_sha256":"305420f9f64aa31a"}},{"arxiv_id":"2309.09858","paper":"/paper/unsupervised-open-vocabulary-object","title":"Unsupervised Open-Vocabulary Object Localization in Videos","date":"2023-09-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"amazon-science/object-centric-vol","path":"evaluation/grouping_metrics.py","file_url":"https://github.com/amazon-science/object-centric-vol/blob/HEAD/evaluation/grouping_metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"8f59f1b08fa79918","mcp_get_code":{"code_sha256":"8f59f1b08fa79918"}},{"arxiv_id":"2206.01370","paper":"/paper/slot-order-matters-for-compositional-scene","title":"Towards Improving the Generation Quality of Autoregressive Slot VAEs","date":"2022-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pemami4911/segregate-relate-imagine","path":"sri/metrics.py","file_url":"https://github.com/pemami4911/segregate-relate-imagine/blob/HEAD/sri/metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bb452191fb5c6d51","mcp_get_code":{"code_sha256":"bb452191fb5c6d51"}},{"arxiv_id":"2203.11194","paper":"/paper/generating-fast-and-slow-scene-decomposition","title":"Test-time Adaptation with Slot-Centric Models","date":"2022-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mihirp1998/Slot-TTA","path":"models.py","file_url":"https://github.com/mihirp1998/Slot-TTA/blob/HEAD/models.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"35ef97ec2efa13ab","mcp_get_code":{"code_sha256":"35ef97ec2efa13ab"}},{"arxiv_id":"2106.03630","paper":"/paper/efficient-iterative-amortized-inference-for","title":"Efficient Iterative Amortized Inference for Learning Symmetric and Disentangled Multi-Object Representations","date":"2021-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pemami4911/EfficientMORL","path":"lib/metrics.py","file_url":"https://github.com/pemami4911/EfficientMORL/blob/HEAD/lib/metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"541dc5b4ae07fd63","mcp_get_code":{"code_sha256":"541dc5b4ae07fd63"}},{"arxiv_id":"1903.00450","paper":"/paper/multi-object-representation-learning-with","title":"Multi-Object Representation Learning with Iterative Variational Inference","date":"2019-03-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pemami4911/IODINE.pytorch","path":"lib/metrics.py","file_url":"https://github.com/pemami4911/IODINE.pytorch/blob/HEAD/lib/metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"585f3727981bb5df","mcp_get_code":{"code_sha256":"585f3727981bb5df"}},{"arxiv_id":"1901.11390","paper":"/paper/monet-unsupervised-scene-decomposition-and","title":"MONet: Unsupervised Scene Decomposition and Representation","date":"2019-01-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JohannesTheo/multi_object_datasets_torch","path":"segmentation_metrics.py","file_url":"https://github.com/JohannesTheo/multi_object_datasets_torch/blob/HEAD/segmentation_metrics.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":"4278b4b72fbcd18d","mcp_get_code":{"code_sha256":"4278b4b72fbcd18d"}}]}