Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity

Abstract

While deep neural networks show great performance on fitting to the training distribution, improving the networks' generalization performance to the test distribution and robustness to the sensitivity to input perturbations still remain as a challenge. Although a number of mixup based augmentation strategies have been proposed to partially address them, it remains unclear as to how to best utilize the supervisory signal within each input data for mixup from the optimization perspective. We propose a new perspective on batch mixup and formulate the optimal construction of a batch of mixup data maximizing the data saliency measure of each individual mixup data and encouraging the supermodular diversity among the constructed mixup data. This leads to a novel discrete optimization problem minimizing the difference between submodular functions. We also propose an efficient modular approximation based iterative submodular minimization algorithm for efficient mixup computation per each minibatch suitable for minibatch based neural network training. Our experiments show the proposed method achieves the state of the art generalization, calibration, and weakly supervised localization results compared to other mixup methods. The source code is available at https://github.com/snu-mllab/Co-Mixup.

1. Introduction

Deep neural networks have been applied to a wide range of artificial intelligence tasks such as computer vision, natural language processing, and signal processing with remarkable performance (Ren et al., 2015; Devlin et al., 2018; Oord et al., 2016) . However, it has been shown that neural networks have excessive representation capability and can even fit random data (Zhang et al., 2016) . Due to these characteristics, the neural networks can easily overfit to training data and show a large generalization gap when tested on previously unseen data. To improve the generalization performance of the neural networks, a body of research has been proposed to develop regularizers based on priors or to augment the training data with task-dependent transforms (Bishop, 2006; Cubuk et al., 2019) . Recently, a new taskindependent data augmentation technique, called mixup, has been proposed (Zhang et al., 2018) . The original mixup, called Input Mixup, linearly interpolates a given pair of input data and can be easily applied to various data and tasks, improving the generalization performance and robustness of neural networks. Other mixup methods, such as manifold mixup (Verma et al., 2019) or CutMix (Yun et al., 2019) , have also been proposed addressing different ways to mix a given pair of input data. Puzzle Mix (Kim et al., 2020) utilizes saliency information and local statistics to ensure mixup data to have rich supervisory signals. However, these approaches only consider mixing a given random pair of input data and do not fully utilize the rich informative supervisory signal in training data including collection of object saliency, relative arrangement, etc. In this work, we simultaneously consider mixmatching different salient regions among all input data so that each generated mixup example accumulates as many salient regions from multiple input data as possible while ensuring Correspondence to: Hyun Oh Song.

