Meta-Learning with Network Pruning for Overfitting Reduction

25 Sep 2019  ·  Hongduan Tian, Bo Liu, Xiao-Tong Yuan, Qingshan Liu ·

Meta-Learning has achieved great success in few-shot learning. However, the existing meta-learning models have been evidenced to overfit on meta-training tasks when using deeper and wider convolutional neural networks. This means that we cannot improve the meta-generalization performance by merely deepening or widening the networks. To remedy such a deficiency of meta-overfitting, we propose in this paper a sparsity constrained meta-learning approach to learn from meta-training tasks a subnetwork from which first-order optimization methods can quickly converge towards the optimal network in meta-testing tasks. Our theoretical analysis shows the benefit of sparsity for improving the generalization gap of the learned meta-initialization network. We have implemented our approach on top of the widely applied Reptile algorithm assembled with varying network pruning routines including Dense-Sparse-Dense (DSD) and Iterative Hard Thresholding (IHT). Extensive experimental results on benchmark datasets with different over-parameterized deep networks demonstrate that our method can not only effectively ease meta-overfitting but also in many cases improve the meta-generalization performance when applied to few-shot classification tasks.

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