{"url":"/sota/continual-learning-on-aids","task":{"name":"Continual Learning","url":"/task/continual-learning","note":null},"dataset":{"name":"AIDS","url":"/dataset/aids"},"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":["1:3 Accuracy"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"1:3 Accuracy":"higher"}},"counts":{"rows":1,"rows_with_code":1,"rows_with_paper_page":1,"rows_dated":1,"rows_using_additional_data":1},"rows":[{"rank_in_archive_order":1,"model":"TEST","metrics":{"1:3 Accuracy":"2"},"uses_additional_data":true,"paper_date":"2025-03-31","paper":"/paper/advances-and-challenges-in-foundation-agents","paper_url":"https://arxiv.org/abs/2504.01990v1","paper_title":"Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems","code":"https://github.com/foundationagents/awesome-foundation-agents","n_code_links":1,"syntology":null}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"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":0,"rows_with_any_sample_ran":0,"distinct_papers_with_graph_line":0,"distinct_papers_with_any_sample_ran":0,"samples_over_distinct_papers":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"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"}}}