New Compression Method Enables Conditional GANs on Edge Devices

A research team from MIT, Adobe Research, and Shanghai Jiao Tong University have introduced a novel method for reducing the cost and size of Conditional GAN generators. Generative Adversarial Networks (GAN) excel at synthesizing photorealistic images. Conditional GANS, or cGANs, provide more controllable image synthesis and enable many computer vision and graphics applications, for example motion transfer of a dance video to a different person, creating VR facial animations for remote social interaction, etc. The problem is, cGANs are notoriously computationally intensive, and this prevents them from being deployed on edge devices like mobile phones, tablets or VR headsets with insufficient hardware resources, memory or power. GAN Compression, the general-purpose compression method the team presents in their paper, has proven effective across different supervision settings (paired and unpaired), model architectures, and learning methods (e.g.