Abstract: Generative adversarial networks (GANs) have been widely studied for unpaired image-to-image translation in recent years. On the other hand, state-of-the-art translation GANs are often constrained by large model sizes and inflexibility in translating across various domains. Inspired by the observation that the mappings between two domains are often approximately invertible, we design an innovative reconfigurable GAN (RF-GAN) that has a small size but is versatile in high-fidelity image translation either across two domains or among multiple domains. One unique feature of RF-GAN lies with its single generator which is reconfigurable and can perform bidirectional image translations by swapping its parameters. In addition, a multi-domain discriminator is designed which allows joint discrimination of original and translated samples in multiple domains. Experiments over eight unpaired image translation datasets (on various tasks such as object transfiguration, season transfer, and painters' style transfer, etc.) show that RF-GAN reduces the model size by up to 75% as compared with state-of-the-art translation GANs but produces superior image translation performance with lower Fréchet Inception Distance consistently.