Integral Continual Learning Along the Tangent Vector Field of Tasks

• URL: http://arxiv.org/abs/2211.13108v3
• Date: Tue, 12 Dec 2023 03:52:00 GMT
• Title: Integral Continual Learning Along the Tangent Vector Field of Tasks
• Authors: Tian Yu Liu, Aditya Golatkar, Stefano Soatto, Alessandro Achille
• Abstract summary: We propose a lightweight continual learning method which incorporates information from specialized datasets incrementally. It maintains a small fixed-size memory buffer, as low as 0.4% of the source datasets, which is updated by simple resampling. Our method achieves strong performance across various buffer sizes for different datasets.
• Score: 112.02761912526734
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a lightweight continual learning method which incorporates information from specialized datasets incrementally, by integrating it along the vector field of "generalist" models. The tangent plane to the specialist model acts as a generalist guide and avoids the kind of over-fitting that leads to catastrophic forgetting, while exploiting the convexity of the optimization landscape in the tangent plane. It maintains a small fixed-size memory buffer, as low as 0.4% of the source datasets, which is updated by simple resampling. Our method achieves strong performance across various buffer sizes for different datasets. Specifically, in the class-incremental setting we outperform the existing methods that do not require distillation by an average of 18.77% and 28.48%, for Seq-CIFAR-10 and Seq-TinyImageNet respectively. Our method can easily be used in conjunction with existing replay-based continual learning methods. When memory buffer constraints are relaxed to allow storage of metadata such as logits, we attain an error reduction of 17.84% towards the paragon performance on Seq-CIFAR-10.

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