A Latent Variables Augmentation Method Based on Adversarial Training for Image Categorization with Insufficient Training Samples

Abstract

In the past few years, people have made great progress in image categorization based on convolutional neural networks (CNN) with the large-scale training set. However, in real-world applications, we may only have insufficient training samples for training CNN. To solve this problem, in this paper, we propose a latent variable augmentation method based on adversarial training (Lagat) to increase the categorization accuracy of CNN with insufficient training samples. In the proposed Lagat model, we propose a uniform loss function, where we take the following two tasks into account. Firstly, we consider the task that we augment latent variables (LVs) from a set of class-specific adaptive distributions and we propose constraints to adjust these LVs distributions. Secondly, we use these sampled LVs to train a predictive image classifier. Moreover, we present an alternative two-play minimization game to optimize this uniform loss function. In addition, the experiment results also demonstrate that the proposed Lagat method delivers higher accuracy than the existing state-of-the-art methods. The source code of the Lagat algorithm is available in Github <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> https://github.com/itapty-ily/Lagat.