Generative adversarial networks (GANs) have been successfully used for considerable computer vision tasks, especially the image-to-image translation. However, generators in these networks are of complicated architectures with large number of parameters and huge computational complexities. Existing methods are mainly designed for compressing and speeding-up deep neural networks in the classification task, and cannot be directly applied on GANs for image translation, due to their different objectives and training procedures. To this end, we develop a novel co-evolutionary approach for reducing their memory usage and FLOPs simultaneously. In practice, generators for two image domains are encoded as two populations and synergistically optimized for investigating the most important convolution filters iteratively. Fitness of each individual is calculated using the number of parameters, a discriminator-aware regularization, and the cycle consistency. Extensive experiments conducted on benchmark datasets demonstrate the effectiveness of the proposed method for obtaining compact and effective generators.

本文针对图像翻译中具有复杂架构、参数数量众多、计算复杂度较高的生成器，提出了一种全新的协同进化方法，以减少其内存使用量及乘法操作次数，从而在图像翻译中实现紧凑高效的生成器设计。Experiments表明，该方法的有效性得到了充分的验证。

非配对图像翻译的共同演化压缩