Image generation remains a fundamental problem of artificial intelligence, specifically in deep learning. The generative adversarial network (GAN) architecture was successful in generating high-quality samples of natural images. We propose a model called composite generative adversarial network (CGAN), that disentangles complicated factors of images with multiple generators in which each generator generates some part of the image. Those parts are combined by an alpha blending process to create a new single image. For example, it can generate background, face, and hair sequentially with three generators trained on face images. There is no supervision on what each generator should generate. The CGAN assigns roles for each generator by factorizing the common factors of images and creates realistic samples as good as GAN. Also, we combined a variational autoencoder with CGAN to visualize the sub-manifolds of latent space learned.
A relevant generative modelling approach is by Kwak et al. REF who proposed a model in which parts of the
Generating Images Part by Part with Composite Generative Adversarial Networks
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Computer Science