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arXiv · 2011.01168

Bilevel Continual Learning

Ammar Shaker·Francesco Alesiani·Shujian Yu·Wenzhe Yin
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Abstract

Continual learning (CL) studies the problem of learning a sequence of tasks, one at a time, such that the learning of each new task does not lead to the deterioration in performance on the previously seen ones while exploiting previously learned features. This paper presents Bilevel Continual Learning (BiCL), a general framework for continual learning that fuses bilevel optimization and recent advances in meta-learning for deep neural networks. BiCL is able to train both deep discriminative and generative models under the conservative setting of the online continual learning. Experimental results show that BiCL provides competitive performance in terms of accuracy for the current task while reducing the effect of catastrophic forgetting. This is a concurrent work with [1]. We submitted it to AAAI 2020 and IJCAI 2020. Now we put it on the arxiv for record. Different from [1], we also consider continual generative model as well. At the same time, the authors are aware of a recent proposal on bilevel optimization based coreset construction for continual learning [2]. [1] Q. Pham, D. Sahoo, C. Liu, and S. C. Hoi. Bilevel continual learning. arXiv preprint arXiv:2007.15553, 2020. [2] Z. Borsos, M. Mutny, and A. Krause. Coresets via bilevel optimization for continual learning and streaming. arXiv preprint arXiv:2006.03875, 2020

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Ammar Shaker, Francesco Alesiani, Shujian Yu, Wenzhe Yin. 2020-11-02. Bilevel Continual Learning. https://arxiv.org/abs/2011.01168

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