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Generative adversarial networks (GANs) have shown remarkable success ingeneration of unstructured data, such as, natural images. However, discoveryand separation of modes in the generated space, essential for several tasksbeyond naive data generation, is still a challenge. In this paper, we addressthe problem of imposing desired modal properties on the generated space using alatent distribution, engineered in accordance with the modal properties of thetrue data distribution. This is achieved by training a latent space inversionnetwork in tandem with the generative network using a divergence loss. Thelatent space is made to follow a continuous multimodal distribution generatedby reparameterization of a pair of continuous and discrete random variables. Inaddition, the modal priors of the latent distribution are learned to match withthe true data distribution using minimal-supervision with negligible incrementin number of learnable parameters. We validate our method on multiple taskssuch as mode separation, conditional generation, and attribute discovery onmultiple real world image datasets and demonstrate its efficacy over otherstate-of-the-art methods.

Mode matching in GANs through latent space learning and inversion