  Mixup linearly interpolates pairs of examples to form new samples, which is
easy to implement and has been shown to be effective in image classification
tasks. However, there are two drawbacks in mixup: one is that more training
epochs are needed to obtain a well-trained model; the other is that mixup
requires tuning a hyper-parameter to gain appropriate capacity but that is a
difficult task. In this paper, we find that mixup constantly explores the
representation space, and inspired by the exploration-exploitation dilemma in
reinforcement learning, we propose mixup Without hesitation (mWh), a concise,
effective, and easy-to-use training algorithm. We show that mWh strikes a good
balance between exploration and exploitation by gradually replacing mixup with
basic data augmentation. It can achieve a strong baseline with less training
time than original mixup and without searching for optimal hyper-parameter,
i.e., mWh acts as mixup without hesitation. mWh can also transfer to CutMix,
and gain consistent improvement on other machine learning and computer vision
tasks such as object detection. Our code is open-source and available at
https://github.com/yuhao318/mwh
