1 research outputs found
Information Compensation for Deep Conditional Generative Networks
In recent years, unsupervised/weakly-supervised conditional generative
adversarial networks (GANs) have achieved many successes on the task of
modeling and generating data. However, one of their weaknesses lies in their
poor ability to separate, or disentangle, the different factors that
characterize the representation encoded in their latent space. To address this
issue, we propose a novel structure for unsupervised conditional GANs powered
by a novel Information Compensation Connection (IC-Connection). The proposed
IC-Connection enables GANs to compensate for information loss incurred during
deconvolution operations. In addition, to quantify the degree of
disentanglement on both discrete and continuous latent variables, we design a
novel evaluation procedure. Our empirical results suggest that our method
achieves better disentanglement compared to the state-of-the-art GANs in a
conditional generation setting