Semi-supervised FusedGAN for Conditional Image Generation

Navaneeth Bodla, Gang Hua, Rama Chellappa; The European Conference on Computer Vision (ECCV), 2018, pp. 669-683


We present FusedGAN, a deep network for conditional image synthesis with controllable sampling of diverse images. Fidelity, diversity and controllable sampling are the main quality measures of a good image generation model. Most existing models are insufficient in all three aspects. The FusedGAN can perform controllable sampling of diverse images with very high fidelity. We argue that controllability can be achieved by disentangling the generation process into various stages. In contrast to stacked GANs, where multiple stages of GANs are trained separately with full supervision of labeled intermediate images, the FusedGAN has a single stage pipeline with a built-in stacking of GANs. Also another limitation of the existing methods is that they require fully supervised training data in the form of paired condition and image. For tasks such as text-to-image generation, the limited size of such fully supervised, paired training datasets may easily cause mode collapsing in training, which confronts the generation of diverse samples. Unlike existing methods, which requires full supervision with paired conditions and images, the FusedGAN can effectively leverage more abundant images without corresponding conditions in training, to produce more diverse samples with high fidelity. We achieve this by fusing two generators: one for unconditional image generation, and the other for conditional image generation, where the two partly share a common latent space thereby disentangling the generation. We demonstrate the efficacy of the FusedGAN in fine grained image generation tasks such as text-to-image, and attribute-to-face generation.

Related Material

author = {Bodla, Navaneeth and Hua, Gang and Chellappa, Rama},
title = {Semi-supervised FusedGAN for Conditional Image Generation},
booktitle = {The European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}