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Single image rain removal plays an important role in numerous multimedia applications. Existing algorithms usually tackle the deraining problem by the way of signal removal, which lead to over-smoothness and generate unexpected artifacts in de-rained images. This paper addresses the deraining problem from a completely different perspective of feature-wise disentanglement, and introduces the interactions and constraints between two disentangled latent spaces. Specifically, we propose an Asynchronous Interactive Generative Adversarial Network (AI-GAN) to progressively disentangle the rainy image into background and rain spaces in feature level through a two-branch structure. Each branch employs a two-stage synthesis strategy and interacts asynchronously by exchanging feed-forward information and sharing feedback gradients, achieving complementary adversarial optimization. This ‘adversarial’ is not …