Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis

CVPR, pp. 1429-1437, 2019.

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We demonstrate the generalizability of the proposed method on three different conditional generation tasks including categorical generation, image-to-image translation, and text-to-image synthesis

Abstract:

Most conditional generation tasks expect diverse outputs given a single conditional context. However, conditional generative adversarial networks (cGANs) often focus on the prior conditional information and ignore the input noise vectors, which contribute to the output variations. Recent attempts to resolve the mode collapse issue for cGA...More

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