AutoMix: Unveiling the Power of Mixup for Stronger Classifiers

Understand

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 directly.