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Generative Adversarial Networks (GANs) play an increasingly important role inmachine learning. However, there is one fundamental issue hindering theirpractical applications: the absence of capability for encoding real-worldsamples. The conventional way of addressing this issue is to learn an encoderfor GAN via Variational Auto-Encoder (VAE). In this paper, we show that theentanglement of the latent space for the VAE/GAN framework poses the mainchallenge for encoder learning. To address the entanglement issue and enableinference in GAN we propose a novel algorithm named Latently InvertibleAutoencoder (LIA). The framework of LIA is that an invertible network and itsinverse mapping are symmetrically embedded in the latent space of VAE. Thedecoder of LIA is first trained as a standard GAN with the invertible networkand then the partial encoder is learned from a disentangled autoencoder bydetaching the invertible network from LIA, thus avoiding the entanglementproblem caused by the random latent space. Experiments conducted on the FFHQface dataset and three LSUN datasets validate the effectiveness of LIA/GAN.

Disentangled Inference for GANs with Latently Invertible Autoencoder