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Image generation remains a fundamental problem in artificial intelligence ingeneral and deep learning in specific. The generative adversarial network (GAN)was successful in generating high quality samples of natural images. We proposea model called composite generative adversarial network, that reveals thecomplex structure of images with multiple generators in which each generatorgenerates some part of the image. Those parts are combined by alpha blendingprocess to create a new single image. It can generate, for example, backgroundand face sequentially with two generators, after training on face dataset.Training was done in an unsupervised way without any labels about what eachgenerator should generate. We found possibilities of learning the structure byusing this generative model empirically.

Generating Images Part by Part with Composite Generative Adversarial Networks