Web: http://arxiv.org/abs/2103.13027

Sept. 23, 2022, 1:15 a.m. | Zicheng Liu, Siyuan Li, Di Wu, Zihan Liu, Zhiyuan Chen, Lirong Wu, Stan Z. Li

cs.CV updates on arXiv.org arxiv.org

Data mixing augmentation have proved to be effective in improving the
generalization ability of deep neural networks. While early methods mix samples
by hand-crafted policies (e.g., linear interpolation), recent methods utilize
saliency information to match the mixed samples and labels via complex offline
optimization. However, there arises a trade-off between precise mixing policies
and optimization complexity. To address this challenge, we propose a novel
automatic mixup (AutoMix) framework, where the mixup policy is parameterized
and serves the ultimate classification goal …

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