{"url":"/sota/continual-learning-on-20newsgroup-10-tasks","task":{"name":"Continual Learning","url":"/task/continual-learning","note":null},"dataset":{"name":"20Newsgroup (10 tasks)","url":"/dataset/20newsgroup-10-tasks"},"category":"Computer Vision","categories":["Computer Vision","Methodology","Natural Language Processing"],"category_note":null,"description":"**Continual Learning** (also known as **Incremental Learning**, **Life-long Learning**) is a concept to learn a model for a large number of tasks sequentially without forgetting knowledge obtained from the preceding tasks, where the data in the old tasks are not available anymore during training new ones.  \r\nIf not mentioned, the benchmarks here are **Task-CL**, where task-id is provided on validation.\r\n\r\nSource:  \r\n[Continual Learning by Asymmetric Loss Approximation with Single-Side Overestimation](https://arxiv.org/abs/1908.02984)  \r\n[Three scenarios for continual learning](https://arxiv.org/abs/1904.07734)  \r\n[Lifelong Machine Learning](https://books.google.ca/books/about/Lifelong_Machine_Learning.html?id=JQ5pDwAAQBAJ&redir_esc=y)  \r\n[Continual lifelong learning with neural networks: A review](https://www.sciencedirect.com/science/article/pii/S0893608019300231)","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["F1 - macro"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"F1 - macro":"higher"}},"counts":{"rows":6,"rows_with_code":6,"rows_with_paper_page":6,"rows_dated":6,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"CTR","metrics":{"F1 - macro":"0.9523"},"uses_additional_data":false,"paper_date":"2021-12-05","paper":"/paper/achieving-forgetting-prevention-and-knowledge-1","paper_url":"https://arxiv.org/abs/2112.02706v1","paper_title":"Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning","code":"https://github.com/zixuanke/pycontinual","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"HAT","metrics":{"F1 - macro":"0.9521"},"uses_additional_data":false,"paper_date":"2018-01-04","paper":"/paper/overcoming-catastrophic-forgetting-with-hard","paper_url":"http://arxiv.org/abs/1801.01423v3","paper_title":"Overcoming catastrophic forgetting with hard attention to the task","code":"https://github.com/joansj/hat","n_code_links":2,"syntology":null},{"rank_in_archive_order":3,"model":"CAT","metrics":{"F1 - macro":"0.9516"},"uses_additional_data":false,"paper_date":"2021-12-18","paper":"/paper/continual-learning-of-a-mixed-sequence-of-1","paper_url":"https://arxiv.org/abs/2112.10017v1","paper_title":"Continual Learning of a Mixed Sequence of Similar and Dissimilar Tasks","code":"https://github.com/zixuanke/pycontinual","n_code_links":2,"syntology":null},{"rank_in_archive_order":4,"model":"B-CL","metrics":{"F1 - macro":"0.9504"},"uses_additional_data":false,"paper_date":"2021-12-06","paper":"/paper/adapting-bert-for-continual-learning-of-a-1","paper_url":"https://arxiv.org/abs/2112.03271v1","paper_title":"Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks","code":"https://github.com/zixuanke/pycontinual","n_code_links":1,"syntology":null},{"rank_in_archive_order":5,"model":"EWC","metrics":{"F1 - macro":"0.9180"},"uses_additional_data":false,"paper_date":"2016-12-02","paper":"/paper/overcoming-catastrophic-forgetting-in-neural","paper_url":"http://arxiv.org/abs/1612.00796v2","paper_title":"Overcoming catastrophic forgetting in neural networks","code":"https://github.com/ContinualAI/avalanche","n_code_links":29,"syntology":{"n_ran":14,"n_unverified":8,"n_samples":22,"n_pointer_only_licence":4}},{"rank_in_archive_order":6,"model":"LAMOL","metrics":{"F1 - macro":"0.4572"},"uses_additional_data":false,"paper_date":"2019-09-07","paper":"/paper/lamal-language-modeling-is-all-you-need-for","paper_url":"https://arxiv.org/abs/1909.03329v2","paper_title":"LAMOL: LAnguage MOdeling for Lifelong Language Learning","code":"https://github.com/jojotenya/LAMOL","n_code_links":1,"syntology":{"n_ran":1,"n_unverified":9,"n_samples":10,"n_pointer_only_licence":0}}],"since_archive":{"claim":"Results that newer papers report for their own method, placed here by Syntology. A model pointed at the cell in the paper's own table; the number was read from that cell and checked against this leaderboard's metric, dataset, split and scale; an independent check that saw this leaderboard's other rows and every other leaderboard on the same dataset accepted it. Not reviewed by the paper's authors or by the archive's editors, and not ranked against the archive rows.","extraction_file_present":true,"measurement":{"test_papers":883,"papers_with_output":881,"judged_true":108,"judged":110,"wilson95_lower":0.9361,"measured_on":"2026-09-24","frozen_commit":"0e3de0df94"},"measurement_note":"blind adjudication of accepted entries on a held-out split of archive papers, rules frozen before the test","coverage":{"sentence":"Syntology has checked 6,264 of the 9,581 papers on this site that are newer than the archive; results from the others appear after they are checked.","complete":false,"papers_newer_than_archive":9581,"papers_checked":6264,"papers_extracted_not_yet_verified":0,"boards_without_verdict":2,"papers_not_yet_extracted":3316},"order":"newest first by month (arXiv date, else the arXiv-id month), then arXiv id descending","columns":[],"entries":[]},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":2,"rows_with_any_sample_ran":2,"distinct_papers_with_graph_line":2,"distinct_papers_with_any_sample_ran":2,"samples_over_distinct_papers":{"n_ran":15,"n_unverified":17,"n_samples":32,"n_pointer_only_licence":4,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":15,"n_unverified":17,"n_samples":32,"n_pointer_only_licence":4,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}