Generative Adversarial Network with Multi-branch Discriminator for imbalanced cross-species image-to-image translation.

There has been an increased interest in high-level image-to-image translation to achieve semantic matching. Through a powerful translation model, we can efficiently synthesize high-quality images with diverse appearances while retaining semantic matching. In this paper, we address an imbalanced learning problem using a cross-species image-to-image translation. We aim to perform the data augmentation through the image translation to boost the recognition performance of imbalanced learning. It requires a strong ability of the model to perform a biomorphic transformation on a semantic level. To tackle this problem, we propose a novel, simple, and effective structure of Multi-Branch Discriminator (termed as MBD) based on Generative Adversarial Networks (GANs). We demonstrate the effectiveness of the proposed MBD through theoretical analysis as well as empirical evaluation. We provide theoretical proof of why the proposed MBD is an effective and optimal case to achieve remarkable performance. Comprehensive experiments on various cross-species image translation tasks illustrate that our MBD can dramatically promote the performance of popular GANs with state-of-the-art results in terms of both objective and subjective assessments. Extensive downstream image recognition evaluations at a few-shot setting have also been conducted to demonstrate that the proposed method can effectively boost the performance of imbalanced learning.