{"about":{"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.","site":"https://codewithpapers.app","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","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/class-incremental-learning/papers/2","list_of":"/task/class-incremental-learning","task":"Class Incremental Learning","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":2,"pages_in_order":7,"rows_per_page":100,"rows":[101,200],"of":634,"counts":{"archive_papers_tagged":634,"with_a_code_link":296,"where_syntology_ran_a_sample":109,"not_listed_spam_title":0,"listed":634,"listed_where_code_ran":109,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":100,"every_run_a_failure_of_syntologys_instrument":9,"listed_with_a_run_with_no_instrument_failure":100,"listed_every_run_a_failure_of_syntologys_instrument":9,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/class-incremental-learning","prev":"/task/class-incremental-learning","next":"/task/class-incremental-learning/papers/3","papers":[{"url":"/paper/replay-and-forget-free-graph-class","slug":"replay-and-forget-free-graph-class","title":"Replay-and-Forget-Free Graph Class-Incremental Learning: A Task Profiling and Prompting Approach","date":"2024-10-14","arxiv_id":"2410.10341","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/replay-and-forget-free-graph-class#ran","syntology_url":"https://syntology.ai/paper/2410.10341","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.10341"}},"official":{"repos":["mala-lab/tpp"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/foundation-model-powered-3d-few-shot-class","slug":"foundation-model-powered-3d-few-shot-class","title":"Foundation Model-Powered 3D Few-Shot Class Incremental Learning via Training-free Adaptor","date":"2024-10-11","arxiv_id":"2410.09237","repositories_listed":1,"syntology":null},{"url":"/paper/closer-towards-better-representation-learning","slug":"closer-towards-better-representation-learning","title":"CLOSER: Towards Better Representation Learning for Few-Shot Class-Incremental Learning","date":"2024-10-08","arxiv_id":"2410.05627","repositories_listed":1,"syntology":null},{"url":"/paper/task-recency-bias-strikes-back-adapting","slug":"task-recency-bias-strikes-back-adapting","title":"Task-recency bias strikes back: Adapting covariances in Exemplar-Free Class Incremental Learning","date":"2024-09-26","arxiv_id":"2409.18265","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/task-recency-bias-strikes-back-adapting#ran","syntology_url":"https://syntology.ai/paper/2409.18265","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.18265"}},"official":{"repos":["grypesc/adagauss"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/a-preliminary-study-on-continual-learning-in","slug":"a-preliminary-study-on-continual-learning-in","title":"A preliminary study on continual learning in computer vision using Kolmogorov-Arnold Networks","date":"2024-09-20","arxiv_id":"2409.13550","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-adapter-routing-for-long-tailed","slug":"adaptive-adapter-routing-for-long-tailed","title":"Adaptive Adapter Routing for Long-Tailed Class-Incremental Learning","date":"2024-09-11","arxiv_id":"2409.07446","repositories_listed":1,"syntology":{"n":14,"n_ran":9,"n_constructed":0,"n_ran_checked":3,"n_instrument":6,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":7,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 6 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/adaptive-adapter-routing-for-long-tailed#ran","syntology_url":"https://syntology.ai/paper/2409.07446","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.07446"}},"official":{"repos":["vita-qzh/apart"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-effective-authorship-attribution","slug":"towards-effective-authorship-attribution","title":"Towards Effective Authorship Attribution: Integrating Class-Incremental Learning","date":"2024-08-12","arxiv_id":"2408.08900","repositories_listed":1,"syntology":null},{"url":"/paper/multi-site-class-incremental-learning-with","slug":"multi-site-class-incremental-learning-with","title":"Multi-Site Class-Incremental Learning with Weighted Experts in Echocardiography","date":"2024-07-31","arxiv_id":"2407.21577","repositories_listed":1,"syntology":null},{"url":"/paper/pip-prototypes-injected-prompt-for-federated","slug":"pip-prototypes-injected-prompt-for-federated","title":"PIP: Prototypes-Injected Prompt for Federated Class Incremental Learning","date":"2024-07-30","arxiv_id":"2407.20705","repositories_listed":1,"syntology":null},{"url":"/paper/ftf-er-feature-topology-fusion-based","slug":"ftf-er-feature-topology-fusion-based","title":"FTF-ER: Feature-Topology Fusion-Based Experience Replay Method for Continual Graph Learning","date":"2024-07-28","arxiv_id":"2407.19429","repositories_listed":1,"syntology":null},{"url":"/paper/clip-with-generative-latent-replay-a-strong","slug":"clip-with-generative-latent-replay-a-strong","title":"CLIP with Generative Latent Replay: a Strong Baseline for Incremental Learning","date":"2024-07-22","arxiv_id":"2407.15793","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":2,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/clip-with-generative-latent-replay-a-strong#ran","syntology_url":"https://syntology.ai/paper/2407.15793","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.15793"}},"official":{"repos":["aimagelab/mammoth"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/class-incremental-learning-with-clip-adaptive","slug":"class-incremental-learning-with-clip-adaptive","title":"Class-Incremental Learning with CLIP: Adaptive Representation Adjustment and Parameter Fusion","date":"2024-07-19","arxiv_id":"2407.14143","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/class-incremental-learning-with-clip-adaptive#ran","syntology_url":"https://syntology.ai/paper/2407.14143","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.14143"}},"official":{"repos":["linlany/rapf"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/continual-learning-for-remote-physiological","slug":"continual-learning-for-remote-physiological","title":"Continual Learning for Remote Physiological Measurement: Minimize Forgetting and Simplify Inference","date":"2024-07-19","arxiv_id":"2407.13974","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/continual-learning-for-remote-physiological#ran","syntology_url":"https://syntology.ai/paper/2407.13974","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.13974"}},"official":{"repos":["mayyoy/rppgdil"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/cromo-mixup-augmenting-cross-model","slug":"cromo-mixup-augmenting-cross-model","title":"CroMo-Mixup: Augmenting Cross-Model Representations for Continual Self-Supervised Learning","date":"2024-07-16","arxiv_id":"2407.12188","repositories_listed":1,"syntology":null},{"url":"/paper/mitigating-background-shift-in-class","slug":"mitigating-background-shift-in-class","title":"Mitigating Background Shift in Class-Incremental Semantic Segmentation","date":"2024-07-16","arxiv_id":"2407.11859","repositories_listed":1,"syntology":null},{"url":"/paper/inemo-incremental-neural-mesh-models-for","slug":"inemo-incremental-neural-mesh-models-for","title":"iNeMo: Incremental Neural Mesh Models for Robust Class-Incremental Learning","date":"2024-07-12","arxiv_id":"2407.09271","repositories_listed":1,"syntology":null},{"url":"/paper/exemplar-free-continual-representation","slug":"exemplar-free-continual-representation","title":"Exemplar-free Continual Representation Learning via Learnable Drift Compensation","date":"2024-07-11","arxiv_id":"2407.08536","repositories_listed":1,"syntology":{"n":16,"n_ran":8,"n_constructed":6,"n_ran_checked":8,"n_instrument":0,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":16,"phrase":"8 ran (of which 6 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/exemplar-free-continual-representation#ran","syntology_url":"https://syntology.ai/paper/2407.08536","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.08536"}},"official":{"repos":["alviur/ldc"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":6,"n_ran_no_instrument_failure":8,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/rethinking-few-shot-class-incremental-1","slug":"rethinking-few-shot-class-incremental-1","title":"Rethinking Few-shot Class-incremental Learning: Learning from Yourself","date":"2024-07-10","arxiv_id":"2407.07468","repositories_listed":1,"syntology":null},{"url":"/paper/tacle-task-and-class-aware-exemplar-free-semi","slug":"tacle-task-and-class-aware-exemplar-free-semi","title":"TACLE: Task and Class-aware Exemplar-free Semi-supervised Class Incremental Learning","date":"2024-07-10","arxiv_id":"2407.08041","repositories_listed":1,"syntology":null},{"url":"/paper/mamba-fscil-dynamic-adaptation-with-selective","slug":"mamba-fscil-dynamic-adaptation-with-selective","title":"Mamba-FSCIL: Dynamic Adaptation with Selective State Space Model for Few-Shot Class-Incremental Learning","date":"2024-07-08","arxiv_id":"2407.06136","repositories_listed":1,"syntology":{"n":8,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":8,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/mamba-fscil-dynamic-adaptation-with-selective#ran","syntology_url":"https://syntology.ai/paper/2407.06136","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.06136"}},"official":{"repos":["xiaojieli0903/mamba-fscil"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/opencil-benchmarking-out-of-distribution","slug":"opencil-benchmarking-out-of-distribution","title":"OpenCIL: Benchmarking Out-of-Distribution Detection in Class-Incremental Learning","date":"2024-07-08","arxiv_id":"2407.06045","repositories_listed":1,"syntology":null},{"url":"/paper/mind-the-interference-retaining-pre-trained","slug":"mind-the-interference-retaining-pre-trained","title":"Mind the Interference: Retaining Pre-trained Knowledge in Parameter Efficient Continual Learning of Vision-Language Models","date":"2024-07-07","arxiv_id":"2407.05342","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":5,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/mind-the-interference-retaining-pre-trained#ran","syntology_url":"https://syntology.ai/paper/2407.05342","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.05342"}},"official":{"repos":["lloongx/diki"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/teal-new-selection-strategy-for-small-buffers","slug":"teal-new-selection-strategy-for-small-buffers","title":"TEAL: New Selection Strategy for Small Buffers in Experience Replay Class Incremental Learning","date":"2024-06-30","arxiv_id":"2407.00673","repositories_listed":1,"syntology":null},{"url":"/paper/ccsi-continual-class-specific-impression-for","slug":"ccsi-continual-class-specific-impression-for","title":"CCSI: Continual Class-Specific Impression for Data-free Class Incremental Learning","date":"2024-06-09","arxiv_id":"2406.05631","repositories_listed":1,"syntology":null},{"url":"/paper/compositional-few-shot-class-incremental","slug":"compositional-few-shot-class-incremental","title":"Compositional Few-Shot Class-Incremental Learning","date":"2024-05-27","arxiv_id":"2405.17022","repositories_listed":1,"syntology":{"n":15,"n_ran":9,"n_constructed":0,"n_ran_checked":6,"n_instrument":3,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 3 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/compositional-few-shot-class-incremental#ran","syntology_url":"https://syntology.ai/paper/2405.17022","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.17022"}},"official":{"repos":["zoilsen/comp-fscil"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/few-shot-tuning-of-foundation-models-for","slug":"few-shot-tuning-of-foundation-models-for","title":"Few-shot Tuning of Foundation Models for Class-incremental Learning","date":"2024-05-26","arxiv_id":"2405.16625","repositories_listed":1,"syntology":{"n":10,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/few-shot-tuning-of-foundation-models-for#ran","syntology_url":"https://syntology.ai/paper/2405.16625","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.16625"}},"official":{"repos":["shuvenduroy/coact-fscil"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/on-sequential-loss-approximation-for","slug":"on-sequential-loss-approximation-for","title":"On Sequential Loss Approximation for Continual Learning","date":"2024-05-26","arxiv_id":"2405.16498","repositories_listed":1,"syntology":null},{"url":"/paper/less-is-more-summarizing-patch-tokens-for","slug":"less-is-more-summarizing-patch-tokens-for","title":"Less is more: Summarizing Patch Tokens for efficient Multi-Label Class-Incremental Learning","date":"2024-05-24","arxiv_id":"2405.15633","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-class-incremental-learning-from-a","slug":"rethinking-class-incremental-learning-from-a","title":"Rethinking Class-Incremental Learning from a Dynamic Imbalanced Learning Perspective","date":"2024-05-24","arxiv_id":"2405.15157","repositories_listed":1,"syntology":null},{"url":"/paper/mitigating-interference-in-the-knowledge","slug":"mitigating-interference-in-the-knowledge","title":"Mitigating Interference in the Knowledge Continuum through Attention-Guided Incremental Learning","date":"2024-05-22","arxiv_id":"2405.13978","repositories_listed":1,"syntology":null},{"url":"/paper/feature-expansion-and-enhanced-compression","slug":"feature-expansion-and-enhanced-compression","title":"Feature Expansion and enhanced Compression for Class Incremental Learning","date":"2024-05-13","arxiv_id":"2405.08038","repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-class-incremental-learning-via-2","slug":"few-shot-class-incremental-learning-via-2","title":"Few-Shot Class Incremental Learning via Robust Transformer Approach","date":"2024-05-08","arxiv_id":"2405.05984","repositories_listed":1,"syntology":null},{"url":"/paper/revisiting-neural-networks-for-continual","slug":"revisiting-neural-networks-for-continual","title":"Revisiting Neural Networks for Continual Learning: An Architectural Perspective","date":"2024-04-23","arxiv_id":"2404.14829","repositories_listed":1,"syntology":{"n":19,"n_ran":14,"n_constructed":4,"n_ran_checked":10,"n_instrument":4,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":19,"phrase":"14 ran (of which 4 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 4 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/revisiting-neural-networks-for-continual#ran","syntology_url":"https://syntology.ai/paper/2404.14829","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.14829"}},"official":{"repos":["byyx666/archcraft"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":4,"n_ran_no_instrument_failure":10,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/dynammo-dynamic-model-merging-for-efficient","slug":"dynammo-dynamic-model-merging-for-efficient","title":"DynaMMo: Dynamic Model Merging for Efficient Class Incremental Learning for Medical Images","date":"2024-04-22","arxiv_id":"2404.14099","repositories_listed":1,"syntology":null},{"url":"/paper/calibrating-higher-order-statistics-for-few","slug":"calibrating-higher-order-statistics-for-few","title":"Calibrating Higher-Order Statistics for Few-Shot Class-Incremental Learning with Pre-trained Vision Transformers","date":"2024-04-09","arxiv_id":"2404.06622","repositories_listed":1,"syntology":{"n":15,"n_ran":9,"n_constructed":0,"n_ran_checked":5,"n_instrument":4,"n_unverified":6,"n_honours":2,"n_violates":0,"n_no_contract":3,"n_pointer_only":8,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 2 honoured, 0 violated, 3 with no contract checked; 4 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/calibrating-higher-order-statistics-for-few#ran","syntology_url":"https://syntology.ai/paper/2404.06622","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.06622"}},"official":{"repos":["dipamgoswami/fscil-calibration"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/pre-trained-vision-and-language-transformers","slug":"pre-trained-vision-and-language-transformers","title":"Pre-trained Vision and Language Transformers Are Few-Shot Incremental Learners","date":"2024-04-02","arxiv_id":"2404.02117","repositories_listed":1,"syntology":{"n":14,"n_ran":12,"n_constructed":0,"n_ran_checked":8,"n_instrument":4,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":1,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 4 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/pre-trained-vision-and-language-transformers#ran","syntology_url":"https://syntology.ai/paper/2404.02117","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.02117"}},"official":{"repos":["khu-agi/privilege"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/semantically-shifted-incremental-adapter","slug":"semantically-shifted-incremental-adapter","title":"Semantically-Shifted Incremental Adapter-Tuning is A Continual ViTransformer","date":"2024-03-29","arxiv_id":"2403.19979","repositories_listed":1,"syntology":null},{"url":"/paper/generative-multi-modal-models-are-good-class","slug":"generative-multi-modal-models-are-good-class","title":"Generative Multi-modal Models are Good Class-Incremental Learners","date":"2024-03-27","arxiv_id":"2403.18383","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":4,"n_pointer_only":9,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 1 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/generative-multi-modal-models-are-good-class#ran","syntology_url":"https://syntology.ai/paper/2403.18383","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.18383"}},"official":{"repos":["doubleclass/gmm"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/orco-towards-better-generalization-via","slug":"orco-towards-better-generalization-via","title":"OrCo: Towards Better Generalization via Orthogonality and Contrast for Few-Shot Class-Incremental Learning","date":"2024-03-27","arxiv_id":"2403.18550","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":4,"n_ran_checked":5,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":8,"phrase":"6 ran (of which 4 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/orco-towards-better-generalization-via#ran","syntology_url":"https://syntology.ai/paper/2403.18550","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.18550"}},"official":{"repos":["noorahmedds/orco"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":4,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/ds-al-a-dual-stream-analytic-learning-for","slug":"ds-al-a-dual-stream-analytic-learning-for","title":"DS-AL: A Dual-Stream Analytic Learning for Exemplar-Free Class-Incremental Learning","date":"2024-03-26","arxiv_id":"2403.17503","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":3,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/ds-al-a-dual-stream-analytic-learning-for#ran","syntology_url":"https://syntology.ai/paper/2403.17503","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.17503"}},"official":{"repos":["ZHUANGHP/Analytic-continual-learning"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/aocil-exemplar-free-analytic-online-class","slug":"aocil-exemplar-free-analytic-online-class","title":"F-OAL: Forward-only Online Analytic Learning with Fast Training and Low Memory Footprint in Class Incremental Learning","date":"2024-03-23","arxiv_id":"2403.15751","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":4,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/aocil-exemplar-free-analytic-online-class#ran","syntology_url":"https://syntology.ai/paper/2403.15751","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.15751"}},"official":{"repos":["liuyuchen-cz/f-oal"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/improving-forward-compatibility-in-class","slug":"improving-forward-compatibility-in-class","title":"Improving Forward Compatibility in Class Incremental Learning by Increasing Representation Rank and Feature Richness","date":"2024-03-22","arxiv_id":"2403.15517","repositories_listed":1,"syntology":null},{"url":"/paper/text-enhanced-data-free-approach-for","slug":"text-enhanced-data-free-approach-for","title":"Text-Enhanced Data-free Approach for Federated Class-Incremental Learning","date":"2024-03-21","arxiv_id":"2403.14101","repositories_listed":1,"syntology":{"n":19,"n_ran":11,"n_constructed":0,"n_ran_checked":6,"n_instrument":5,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":19,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 5 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/text-enhanced-data-free-approach-for#ran","syntology_url":"https://syntology.ai/paper/2403.14101","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.14101"}},"official":{"repos":["tmtuan1307/lander"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/ntk-guided-few-shot-class-incremental","slug":"ntk-guided-few-shot-class-incremental","title":"NTK-Guided Few-Shot Class Incremental Learning","date":"2024-03-19","arxiv_id":"2403.12486","repositories_listed":1,"syntology":null},{"url":"/paper/expandable-subspace-ensemble-for-pre-trained","slug":"expandable-subspace-ensemble-for-pre-trained","title":"Expandable Subspace Ensemble for Pre-Trained Model-Based Class-Incremental Learning","date":"2024-03-18","arxiv_id":"2403.12030","repositories_listed":1,"syntology":{"n":7,"n_ran":2,"n_constructed":1,"n_ran_checked":1,"n_instrument":1,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":7,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/expandable-subspace-ensemble-for-pre-trained#ran","syntology_url":"https://syntology.ai/paper/2403.12030","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.12030"}},"official":{"repos":["sun-hailong/cvpr24-ease"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/coleclip-open-domain-continual-learning-via","slug":"coleclip-open-domain-continual-learning-via","title":"CoLeCLIP: Open-Domain Continual Learning via Joint Task Prompt and Vocabulary Learning","date":"2024-03-15","arxiv_id":"2403.10245","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/coleclip-open-domain-continual-learning-via#ran","syntology_url":"https://syntology.ai/paper/2403.10245","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.10245"}},"official":{"repos":["YukunLi99/CoLeCLIP"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/few-shot-class-incremental-learning-with-1","slug":"few-shot-class-incremental-learning-with-1","title":"Few-Shot Class Incremental Learning with Attention-Aware Self-Adaptive Prompt","date":"2024-03-14","arxiv_id":"2403.09857","repositories_listed":1,"syntology":null},{"url":"/paper/focil-finetune-and-freeze-for-online-class","slug":"focil-finetune-and-freeze-for-online-class","title":"FOCIL: Finetune-and-Freeze for Online Class Incremental Learning by Training Randomly Pruned Sparse Experts","date":"2024-03-13","arxiv_id":"2403.14684","repositories_listed":1,"syntology":null},{"url":"/paper/12-mj-per-class-on-device-online-few-shot","slug":"12-mj-per-class-on-device-online-few-shot","title":"12 mJ per Class On-Device Online Few-Shot Class-Incremental Learning","date":"2024-03-12","arxiv_id":"2403.07851","repositories_listed":1,"syntology":null},{"url":"/paper/class-incremental-learning-for-time-series","slug":"class-incremental-learning-for-time-series","title":"Class-incremental Learning for Time Series: Benchmark and Evaluation","date":"2024-02-19","arxiv_id":"2402.12035","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":3,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/class-incremental-learning-for-time-series#ran","syntology_url":"https://syntology.ai/paper/2402.12035","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.12035"}},"official":{"repos":["zqiao11/tscil"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/elastic-feature-consolidation-for-cold-start","slug":"elastic-feature-consolidation-for-cold-start","title":"Elastic Feature Consolidation for Cold Start Exemplar-Free Incremental Learning","date":"2024-02-06","arxiv_id":"2402.03917","repositories_listed":1,"syntology":{"n":13,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":9,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/elastic-feature-consolidation-for-cold-start#ran","syntology_url":"https://syntology.ai/paper/2402.03917","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.03917"}},"official":{"repos":["simomagi/elastic_feature_consolidation"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":9,"ran_from_kinds":["official"]}}},{"url":"/paper/pl-fscil-harnessing-the-power-of-prompts-for","slug":"pl-fscil-harnessing-the-power-of-prompts-for","title":"PL-FSCIL: Harnessing the Power of Prompts for Few-Shot Class-Incremental Learning","date":"2024-01-26","arxiv_id":"2401.14807","repositories_listed":1,"syntology":null},{"url":"/paper/divide-and-not-forget-ensemble-of-selectively","slug":"divide-and-not-forget-ensemble-of-selectively","title":"Divide and not forget: Ensemble of selectively trained experts in Continual Learning","date":"2024-01-18","arxiv_id":"2401.10191","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":4,"n_ran_checked":5,"n_instrument":4,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"9 ran (of which 4 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/divide-and-not-forget-ensemble-of-selectively#ran","syntology_url":"https://syntology.ai/paper/2401.10191","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.10191"}},"official":{"repos":["grypesc/seed"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":4,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/federated-class-incremental-learning-with-1","slug":"federated-class-incremental-learning-with-1","title":"PILoRA: Prototype Guided Incremental LoRA for Federated Class-Incremental Learning","date":"2024-01-04","arxiv_id":"2401.02094","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":10,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/federated-class-incremental-learning-with-1#ran","syntology_url":"https://syntology.ai/paper/2401.02094","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.02094"}},"official":{"repos":["ghy0501/pilora"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-prompt-with-distribution-based","slug":"learning-prompt-with-distribution-based","title":"Learning Prompt with Distribution-Based Feature Replay for Few-Shot Class-Incremental Learning","date":"2024-01-03","arxiv_id":"2401.01598","repositories_listed":1,"syntology":null},{"url":"/paper/dyson-dynamic-feature-space-self-organization","slug":"dyson-dynamic-feature-space-self-organization","title":"DYSON: Dynamic Feature Space Self-Organization for Online Task-Free Class Incremental Learning","date":"2024-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/fcs-feature-calibration-and-separation-for","slug":"fcs-feature-calibration-and-separation-for","title":"FCS: Feature Calibration and Separation for Non-Exemplar Class Incremental Learning","date":"2024-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/generative-multi-modal-models-are-good-class-1","slug":"generative-multi-modal-models-are-good-class-1","title":"Generative Multi-modal Models are Good Class Incremental Learners","date":"2024-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/nice-neurogenesis-inspired-contextual","slug":"nice-neurogenesis-inspired-contextual","title":"NICE: Neurogenesis Inspired Contextual Encoding for Replay-free Class Incremental Learning","date":"2024-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/fine-grained-knowledge-selection-and","slug":"fine-grained-knowledge-selection-and","title":"Fine-Grained Knowledge Selection and Restoration for Non-Exemplar Class Incremental Learning","date":"2023-12-20","arxiv_id":"2312.12722","repositories_listed":1,"syntology":null},{"url":"/paper/read-between-the-layers-leveraging-intra","slug":"read-between-the-layers-leveraging-intra","title":"Read Between the Layers: Leveraging Multi-Layer Representations for Rehearsal-Free Continual Learning with Pre-Trained Models","date":"2023-12-13","arxiv_id":"2312.08888","repositories_listed":1,"syntology":{"n":11,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":5,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/read-between-the-layers-leveraging-intra#ran","syntology_url":"https://syntology.ai/paper/2312.08888","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.08888"}},"official":{"repos":["ky-ah/layup"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/few-shot-class-incremental-learning-via-3","slug":"few-shot-class-incremental-learning-via-3","title":"Few-Shot Class-Incremental Learning via Training-Free Prototype Calibration","date":"2023-12-08","arxiv_id":"2312.05229","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/few-shot-class-incremental-learning-via-3#ran","syntology_url":"https://syntology.ai/paper/2312.05229","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.05229"}},"official":{"repos":["wangkiw/teen"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/class-incremental-learning-for-adversarial","slug":"class-incremental-learning-for-adversarial","title":"Enhancing Robustness in Incremental Learning with Adversarial Training","date":"2023-12-06","arxiv_id":"2312.03289","repositories_listed":1,"syntology":null},{"url":"/paper/mind-multi-task-incremental-network","slug":"mind-multi-task-incremental-network","title":"MIND: Multi-Task Incremental Network Distillation","date":"2023-12-05","arxiv_id":"2312.02916","repositories_listed":1,"syntology":null},{"url":"/paper/prompt-based-exemplar-super-compression-and","slug":"prompt-based-exemplar-super-compression-and","title":"Prompt-Based Exemplar Super-Compression and Regeneration for Class-Incremental Learning","date":"2023-11-30","arxiv_id":"2311.18266","repositories_listed":1,"syntology":null},{"url":"/paper/sketch-input-method-editor-a-comprehensive","slug":"sketch-input-method-editor-a-comprehensive","title":"Sketch Input Method Editor: A Comprehensive Dataset and Methodology for Systematic Input Recognition","date":"2023-11-30","arxiv_id":"2311.18254","repositories_listed":1,"syntology":null},{"url":"/paper/robust-feature-learning-and-global-variance","slug":"robust-feature-learning-and-global-variance","title":"Robust Feature Learning and Global Variance-Driven Classifier Alignment for Long-Tail Class Incremental Learning","date":"2023-11-02","arxiv_id":"2311.01227","repositories_listed":1,"syntology":null},{"url":"/paper/constructing-sample-to-class-graph-for-few","slug":"constructing-sample-to-class-graph-for-few","title":"Constructing Sample-to-Class Graph for Few-Shot Class-Incremental Learning","date":"2023-10-31","arxiv_id":"2310.20268","repositories_listed":1,"syntology":null},{"url":"/paper/look-at-me-no-replay-surprisenet-anomaly","slug":"look-at-me-no-replay-surprisenet-anomaly","title":"Look At Me, No Replay! SurpriseNet: Anomaly Detection Inspired Class Incremental Learning","date":"2023-10-30","arxiv_id":"2310.20052","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-class-incremental-learning-in-the","slug":"rethinking-class-incremental-learning-in-the","title":"Rethinking Class-incremental Learning in the Era of Large Pre-trained Models via Test-Time Adaptation","date":"2023-10-17","arxiv_id":"2310.11482","repositories_listed":1,"syntology":null},{"url":"/paper/openincrement-a-unified-framework-for-open","slug":"openincrement-a-unified-framework-for-open","title":"OpenIncrement: A Unified Framework for Open Set Recognition and Deep Class-Incremental Learning","date":"2023-10-05","arxiv_id":"2310.03848","repositories_listed":1,"syntology":null},{"url":"/paper/cooler-class-incremental-learning-for","slug":"cooler-class-incremental-learning-for","title":"COOLer: Class-Incremental Learning for Appearance-Based Multiple Object Tracking","date":"2023-10-04","arxiv_id":"2310.03006","repositories_listed":1,"syntology":null},{"url":"/paper/pilot-a-pre-trained-model-based-continual","slug":"pilot-a-pre-trained-model-based-continual","title":"PILOT: A Pre-Trained Model-Based Continual Learning Toolbox","date":"2023-09-13","arxiv_id":"2309.07117","repositories_listed":1,"syntology":null},{"url":"/paper/class-incremental-grouping-network-for","slug":"class-incremental-grouping-network-for","title":"Class-Incremental Grouping Network for Continual Audio-Visual Learning","date":"2023-09-11","arxiv_id":"2309.05281","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/class-incremental-grouping-network-for#ran","syntology_url":"https://syntology.ai/paper/2309.05281","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.05281"}},"official":{"repos":["stonemo/cign"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/dynamic-residual-classifier-for-class","slug":"dynamic-residual-classifier-for-class","title":"Dynamic Residual Classifier for Class Incremental Learning","date":"2023-08-25","arxiv_id":"2308.13305","repositories_listed":1,"syntology":{"n":21,"n_ran":12,"n_constructed":6,"n_ran_checked":8,"n_instrument":4,"n_unverified":9,"n_honours":1,"n_violates":0,"n_no_contract":7,"n_pointer_only":21,"phrase":"12 ran (of which 6 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 4 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/dynamic-residual-classifier-for-class#ran","syntology_url":"https://syntology.ai/paper/2308.13305","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.13305"}},"official":{"repos":["chen-xw/drc-cil"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":6,"n_ran_no_instrument_failure":8,"n_unverified":9,"ran_from_kinds":["official"]}}},{"url":"/paper/masked-autoencoders-are-efficient-class","slug":"masked-autoencoders-are-efficient-class","title":"Masked Autoencoders are Efficient Class Incremental Learners","date":"2023-08-24","arxiv_id":"2308.12510","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/masked-autoencoders-are-efficient-class#ran","syntology_url":"https://syntology.ai/paper/2308.12510","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.12510"}},"official":{"repos":["scok30/mae-cil"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/generalized-continual-category-discovery","slug":"generalized-continual-category-discovery","title":"Category Adaptation Meets Projected Distillation in Generalized Continual Category Discovery","date":"2023-08-23","arxiv_id":"2308.12112","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":6,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/generalized-continual-category-discovery#ran","syntology_url":"https://syntology.ai/paper/2308.12112","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.12112"}},"official":{"repos":["grypesc/camp"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/audio-visual-class-incremental-learning","slug":"audio-visual-class-incremental-learning","title":"Audio-Visual Class-Incremental Learning","date":"2023-08-21","arxiv_id":"2308.11073","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":2,"n_instrument":5,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":10,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 5 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/audio-visual-class-incremental-learning#ran","syntology_url":"https://syntology.ai/paper/2308.11073","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.11073"}},"official":{"repos":["weiguopian/av-cil_iccv2023"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/adapt-your-teacher-improving-knowledge","slug":"adapt-your-teacher-improving-knowledge","title":"Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-free Continual Learning","date":"2023-08-18","arxiv_id":"2308.09544","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/adapt-your-teacher-improving-knowledge#ran","syntology_url":"https://syntology.ai/paper/2308.09544","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.09544"}},"official":{"repos":["fszatkowski/cl-teacher-adaptation"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/napa-vq-neighborhood-aware-prototype","slug":"napa-vq-neighborhood-aware-prototype","title":"NAPA-VQ: Neighborhood Aware Prototype Augmentation with Vector Quantization for Continual Learning","date":"2023-08-18","arxiv_id":"2308.09297","repositories_listed":1,"syntology":{"n":18,"n_ran":13,"n_constructed":4,"n_ran_checked":8,"n_instrument":5,"n_unverified":5,"n_honours":1,"n_violates":0,"n_no_contract":7,"n_pointer_only":18,"phrase":"13 ran (of which 4 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 5 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/napa-vq-neighborhood-aware-prototype#ran","syntology_url":"https://syntology.ai/paper/2308.09297","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.09297"}},"official":{"repos":["tamasham/napa-vq"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":4,"n_ran_no_instrument_failure":8,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/online-class-incremental-learning-on","slug":"online-class-incremental-learning-on","title":"Online Class Incremental Learning on Stochastic Blurry Task Boundary via Mask and Visual Prompt Tuning","date":"2023-08-18","arxiv_id":"2308.09303","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-efficient-continual-learning-with","slug":"enhancing-efficient-continual-learning-with","title":"Enhancing Efficient Continual Learning with Dynamic Structure Development of Spiking Neural Networks","date":"2023-08-09","arxiv_id":"2308.04749","repositories_listed":1,"syntology":null},{"url":"/paper/heterogeneous-forgetting-compensation-for","slug":"heterogeneous-forgetting-compensation-for","title":"Heterogeneous Forgetting Compensation for Class-Incremental Learning","date":"2023-08-07","arxiv_id":"2308.03374","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":11,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/heterogeneous-forgetting-compensation-for#ran","syntology_url":"https://syntology.ai/paper/2308.03374","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.03374"}},"official":{"repos":["jiahuadong/hfc"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/balanced-destruction-reconstruction-dynamics","slug":"balanced-destruction-reconstruction-dynamics","title":"Balanced Destruction-Reconstruction Dynamics for Memory-replay Class Incremental Learning","date":"2023-08-03","arxiv_id":"2308.01698","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":6,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/balanced-destruction-reconstruction-dynamics#ran","syntology_url":"https://syntology.ai/paper/2308.01698","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.01698"}},"official":{"repos":["zyuh/bdr-main"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/cbcl-pr-a-cognitively-inspired-model-for","slug":"cbcl-pr-a-cognitively-inspired-model-for","title":"CBCL-PR: A Cognitively Inspired Model for Class-Incremental Learning in Robotics","date":"2023-07-31","arxiv_id":"2308.00199","repositories_listed":1,"syntology":null},{"url":"/paper/proxy-anchor-based-unsupervised-learning-for","slug":"proxy-anchor-based-unsupervised-learning-for","title":"Proxy Anchor-based Unsupervised Learning for Continuous Generalized Category Discovery","date":"2023-07-20","arxiv_id":"2307.10943","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":1,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"3 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/proxy-anchor-based-unsupervised-learning-for#ran","syntology_url":"https://syntology.ai/paper/2307.10943","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.10943"}},"official":{"repos":["hy2mk/cgcd"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/active-class-selection-for-few-shot-class","slug":"active-class-selection-for-few-shot-class","title":"Active Class Selection for Few-Shot Class-Incremental Learning","date":"2023-07-05","arxiv_id":"2307.02641","repositories_listed":1,"syntology":null},{"url":"/paper/s3c-self-supervised-stochastic-classifiers","slug":"s3c-self-supervised-stochastic-classifiers","title":"S3C: Self-Supervised Stochastic Classifiers for Few-Shot Class-Incremental Learning","date":"2023-07-05","arxiv_id":"2307.02246","repositories_listed":1,"syntology":null},{"url":"/paper/class-incremental-learning-based-on-label","slug":"class-incremental-learning-based-on-label","title":"Class-Incremental Learning based on Label Generation","date":"2023-06-22","arxiv_id":"2306.12619","repositories_listed":1,"syntology":null},{"url":"/paper/learnability-and-algorithm-for-continual","slug":"learnability-and-algorithm-for-continual","title":"Learnability and Algorithm for Continual Learning","date":"2023-06-22","arxiv_id":"2306.12646","repositories_listed":1,"syntology":null},{"url":"/paper/knowledge-transfer-driven-few-shot-class","slug":"knowledge-transfer-driven-few-shot-class","title":"Knowledge Transfer-Driven Few-Shot Class-Incremental Learning","date":"2023-06-19","arxiv_id":"2306.10942","repositories_listed":1,"syntology":null},{"url":"/paper/learning-without-forgetting-for-vision","slug":"learning-without-forgetting-for-vision","title":"Learning without Forgetting for Vision-Language Models","date":"2023-05-30","arxiv_id":"2305.19270","repositories_listed":1,"syntology":null},{"url":"/paper/prediction-error-based-classification-for","slug":"prediction-error-based-classification-for","title":"Prediction Error-based Classification for Class-Incremental Learning","date":"2023-05-30","arxiv_id":"2305.18806","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":5,"n_ran_checked":5,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"8 ran (of which 5 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/prediction-error-based-classification-for#ran","syntology_url":"https://syntology.ai/paper/2305.18806","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.18806"}},"official":{"repos":["michalzajac-ml/pec"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":5,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/sharp-sparsity-and-hidden-activation-replay","slug":"sharp-sparsity-and-hidden-activation-replay","title":"SHARP: Sparsity and Hidden Activation RePlay for Neuro-Inspired Continual Learning","date":"2023-05-29","arxiv_id":"2305.18563","repositories_listed":1,"syntology":null},{"url":"/paper/teamwork-is-not-always-good-an-empirical","slug":"teamwork-is-not-always-good-an-empirical","title":"Teamwork Is Not Always Good: An Empirical Study of Classifier Drift in Class-incremental Information Extraction","date":"2023-05-26","arxiv_id":"2305.16559","repositories_listed":1,"syntology":null},{"url":"/paper/2305-14657","slug":"2305-14657","title":"Dealing with Cross-Task Class Discrimination in Online Continual Learning","date":"2023-05-24","arxiv_id":"2305.14657","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/2305-14657#ran","syntology_url":"https://syntology.ai/paper/2305.14657","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.14657"}},"official":{"repos":["gydpku/gsa"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/few-shot-class-incremental-pill-recognition","slug":"few-shot-class-incremental-pill-recognition","title":"A Forward and Backward Compatible Framework for Few-shot Class-incremental Pill Recognition","date":"2023-04-24","arxiv_id":"2304.11959","repositories_listed":1,"syntology":null},{"url":"/paper/preserving-locality-in-vision-transformers","slug":"preserving-locality-in-vision-transformers","title":"Preserving Locality in Vision Transformers for Class Incremental Learning","date":"2023-04-14","arxiv_id":"2304.06971","repositories_listed":1,"syntology":null},{"url":"/paper/continual-learning-for-lidar-semantic","slug":"continual-learning-for-lidar-semantic","title":"Continual Learning for LiDAR Semantic Segmentation: Class-Incremental and Coarse-to-Fine strategies on Sparse Data","date":"2023-04-08","arxiv_id":"2304.03980","repositories_listed":1,"syntology":null},{"url":"/paper/learning-with-fantasy-semantic-aware-virtual","slug":"learning-with-fantasy-semantic-aware-virtual","title":"Learning with Fantasy: Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental Learning","date":"2023-04-02","arxiv_id":"2304.00426","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/learning-with-fantasy-semantic-aware-virtual#ran","syntology_url":"https://syntology.ai/paper/2304.00426","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.00426"}},"official":{"repos":["zysong0113/savc"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}}],"record_sha256":"c8e53bcc699b54068532c92a8eb2b8b5eb5c815a9c96d1613e19919211f5a534","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}