Memory-Reduced Meta-Learning with Guaranteed Convergence

The optimization-based meta-learning approach is gaining increased traction because of its unique ability to quickly adapt to a new task using only small amounts of data. However, existing optimization-based meta-learning approaches, such as MAML, ANIL and their variants, generally employ backpropagation for upper-level gradient estimation, which requires using historical lower-level parameters/gradients and thus increases computational and memory overhead in each iteration. In this paper, we propose a meta-learning algorithm that can avoid using historical parameters/gradients and significantly reduce memory costs in each iteration compared to existing optimization-based meta-learning approaches. In addition to memory reduction, we prove that our proposed algorithm converges sublinearly with the iteration number of upper-level optimization, and the convergence error decays sublinearly with the batch size of sampled tasks. In the specific case in terms of deterministic meta-learning, we also prove that our proposed algorithm converges to an exact solution. Moreover, we quantify the computational complexity of the algorithm, which matches existing convergence results on meta-learning even without using any historical parameters/gradients. Experimental results on meta-learning benchmarks confirm the efficacy of our proposed algorithm.

Authors

• Honglin Yang Department of Automation, Xiamen University Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China
• Ji Ma Department of Automation, Xiamen University Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China
• Xiao Yu Institute of Artificial Intelligence, Xiamen University Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China

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