  Mix-up training approaches have proven to be effective in improving the
generalization ability of Deep Neural Networks. Over the years, the research
community expands mix-up methods into two directions, with extensive efforts to
improve saliency-guided procedures but minimal focus on the arbitrary path,
leaving the randomization domain unexplored. In this paper, inspired by the
superior qualities of each direction over one another, we introduce a novel
method that lies at the junction of the two routes. By combining the best
elements of randomness and saliency utilization, our method balances speed,
simplicity, and accuracy. We name our method R-Mix following the concept of
"Random Mix-up". We demonstrate its effectiveness in generalization, weakly
supervised object localization, calibration, and robustness to adversarial
attacks. Finally, in order to address the question of whether there exists a
better decision protocol, we train a Reinforcement Learning agent that decides
the mix-up policies based on the classifier's performance, reducing dependency
on human-designed objectives and hyperparameter tuning. Extensive experiments
further show that the agent is capable of performing at the cutting-edge level,
laying the foundation for a fully automatic mix-up. Our code is released at
[https://github.com/minhlong94/Random-Mixup].
