Not every sample is efficient: Analogical generative adversarial network for unpaired image-to-image translation

Abstract

Image translation is to learn an effective mapping function that aims to convert an image from a source domain to another target domain. With the proposal and further developments of generative adversarial networks (GANs), the generative models have achieved great breakthroughs. The image-to-image (I2I) translation methods can mainly fall into two categories: Paired and Unpaired. The former paired methods usually require a large amount of input–output sample pairs to perform one-side image translation, which heavily limits its practicability. To address the lack of the paired samples, CycleGAN and its extensions utilize the cycle-consistency loss to provide an elegant and generic solution to perform the unpaired I2I translation between two domains based on unpaired data. This thread of dual learning-based methods usually adopts the random sampling strategy for optimizing and does not consider the content similarity between samples. However, not every sample is efficient and effective for the desired optimization and leads to optimal convergence. Inspired by analogical learning, which is to utilize the relationships and similarities between sample observations, we propose a novel generic metric-based sampling strategy to effectively select samples from different domains for training. Besides, we introduce a novel analogical adversarial loss to force the model to learn from the effective samples and alleviate the influence of the negative samples. Experimental results on various vision tasks have demonstrated the superior performance of the proposed method. The proposed method is also a generic framework that can be easily extended to other I2I translation methods and result in a performance gain.