{"id":"arxiv:2403.05175","title":"Continual Learning and Catastrophic Forgetting","date":"2024-03-08","section":"Data, training, optimization","quality":-0.26995,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3ed9ae586e8898d0235553d62c5f002609d3d112cbec54cc46655d19ff736400/","source_url":"https://arxiv.org/abs/2403.05175","text_available":true,"authors":["Gido M. van de Ven","Nicholas Soures","Dhireesha Kudithipudi"],"outgoing":[{"source":"arxiv:2403.05175","target":"doi:10.1038/s41586-024-07711-7","evidence":"id","id":"doi:10.1038/s41586-024-07711-7","title":"Maintaining Plasticity in Deep Continual Learning","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/b5d46327415fe38f2b9160b874735530939961a71536f7f1ac4c0dc7a3af3224/"},{"source":"arxiv:2403.05175","target":"doi:10.1073/pnas.1611835114","evidence":"title","id":"doi:10.1073/pnas.1611835114","title":"Overcoming catastrophic forgetting in neural 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Learning and Catastrophic Forgetting","offset":0,"level":1,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3ed9ae586e8898d0235553d62c5f002609d3d112cbec54cc46655d19ff736400/part-1.html#text"},{"title":"Abstract","offset":50,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3ed9ae586e8898d0235553d62c5f002609d3d112cbec54cc46655d19ff736400/part-1.html#text"},{"title":"Key Points","offset":709,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3ed9ae586e8898d0235553d62c5f002609d3d112cbec54cc46655d19ff736400/part-1.html#text"},{"title":"1 Introduction","offset":2112,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3ed9ae586e8898d0235553d62c5f002609d3d112cbec54cc46655d19ff736400/part-1.html#text"},{"title":"2 The Continual Learning Problem","offset":6569,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3ed9ae586e8898d0235553d62c5f002609d3d112cbec54cc46655d19ff736400/part-1.html#text"},{"title":"2.1 Catastrophic Forgetting","offset":7260,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3ed9ae586e8898d0235553d62c5f002609d3d112cbec54cc46655d19ff736400/part-1.html#text"},{"title":"2.2 Other Features Important for Continual Learning","offset":9603,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3ed9ae586e8898d0235553d62c5f002609d3d112cbec54cc46655d19ff736400/part-1.html#text"},{"title":"2.3 Task-based versus Task-free Continual Learning","offset":14574,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3ed9ae586e8898d0235553d62c5f002609d3d112cbec54cc46655d19ff736400/part-1.html#text"},{"title":"2.4 Three Continual Learning Scenarios","offset":19697,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3ed9ae586e8898d0235553d62c5f002609d3d112cbec54cc46655d19ff736400/part-2.html#text"},{"title":"Clarifying note.","offset":25058,"level":4,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3ed9ae586e8898d0235553d62c5f002609d3d112cbec54cc46655d19ff736400/part-2.html#text"},{"title":"2.5 Evaluation","offset":25799,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3ed9ae586e8898d0235553d62c5f002609d3d112cbec54cc46655d19ff736400/part-2.html#text"},{"title":"3 Continual Learning Approaches","offset":29238,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3ed9ae586e8898d0235553d62c5f002609d3d112cbec54cc46655d19ff736400/part-2.html#text"},{"title":"3.1 Replay","offset":29743,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3ed9ae586e8898d0235553d62c5f002609d3d112cbec54cc46655d19ff736400/part-2.html#text"},{"title":"3.2 Parameter Regularization","offset":34460,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3ed9ae586e8898d0235553d62c5f002609d3d112cbec54cc46655d19ff736400/part-3.html#text"},{"title":"3.3 Functional Regularization","offset":38226,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3ed9ae586e8898d0235553d62c5f002609d3d112cbec54cc46655d19ff736400/part-3.html#text"},{"title":"3.4 Optimization-based Approaches","offset":43592,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3ed9ae586e8898d0235553d62c5f002609d3d112cbec54cc46655d19ff736400/part-3.html#text"},{"title":"3.5 Context-dependent Processing","offset":48741,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3ed9ae586e8898d0235553d62c5f002609d3d112cbec54cc46655d19ff736400/part-4.html#text"},{"title":"3.6 Template-based Classification","offset":52832,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3ed9ae586e8898d0235553d62c5f002609d3d112cbec54cc46655d19ff736400/part-4.html#text"},{"title":"4 Continual Learning in Deep Learning versus in Cognitive Science","offset":56821,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3ed9ae586e8898d0235553d62c5f002609d3d112cbec54cc46655d19ff736400/part-4.html#text"},{"title":"5 Conclusion","offset":60400,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3ed9ae586e8898d0235553d62c5f002609d3d112cbec54cc46655d19ff736400/part-4.html#text"},{"title":"Acknowledgements","offset":63263,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3ed9ae586e8898d0235553d62c5f002609d3d112cbec54cc46655d19ff736400/part-5.html#text"}],"text_code_points":63548,"source_markdown":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3ed9ae586e8898d0235553d62c5f002609d3d112cbec54cc46655d19ff736400/source.md"}