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Workshop: Machine Learning for Data: Automated Creation, Privacy, Bias

AutoMixup: Learning mix-up policies with Reinforcement Learning

Long Luu · Zeyi Huang · Haohan Wang


Mix-up has been proven efficient in improving model's generalization ability, and multiple extensions of the original mix-up has been introduced in recent years. However, these techniques mainly focus on the data instead of the neural network's performance. In this paper, we propose a new method to automatically learn the mix-up strategy with the gradient information and the reinforcement learning module. The mix-up strategy is controlled by a neural network trained with reinforcement learning to maximize the expected accuracy of the classifier on the validation set. Initial results show a faster convergence rate compared to other mix-up methods.

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