Inverse Adversarial Diversity Learning for Network Ensemble

Network ensemble aims to obtain better results by aggregating the predictions of multiple weak networks, in which how to keep the diversity of different networks plays a critical role in the training process. Many existing approaches keep this kind of diversity either by simply using different network initializations or data partitions, which often requires repeated attempts to pursue a relatively high performance. In this article, we propose a novel inverse adversarial diversity learning (IADL) method to learn a simple yet effective ensemble regime, which can be easily implemented in the following two steps. First, we take each weak network as a generator and design a discriminator to judge the difference between the features extracted by different weak networks. Second, we present an inverse adversarial diversity constraint to push the discriminator to cheat generators that all the resulting features of the same image are too similar to distinguish each other. As a result, diverse features will be extracted by these weak networks through a min–max optimization. What is more, our method can be applied to a variety of tasks, such as image classification and image retrieval, by applying a multitask learning objective function to train all these weak networks in an end-to-end manner. We conduct extensive experiments on the CIFAR-10, CIFAR-100, CUB200-2011, and CARS196 datasets, in which the results show that our method significantly outperforms most of the state-of-the-art approaches.