Guided Variational Autoencoder for Disentanglement Learning

CVPR 2020 Zheng DingYifan XuWeijian XuGaurav ParmarYang YangMax WellingZhuowen Tu

We propose an algorithm, guided variational autoencoder (Guided-VAE), that is able to learn a controllable generative model by performing latent representation disentanglement learning. The learning objective is achieved by providing signals to the latent encoding/embedding in VAE without changing its main backbone architecture, hence retaining the desirable properties of the VAE... (read more)

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