f-VAEs: Improve VAEs with Conditional Flows

In this paper, we integrate VAEs and flow-based generative models successfully and get f-VAEs. Compared with VAEs, f-VAEs generate more vivid images, solved the blurred-image problem of VAEs. Compared with flow-based models such as Glow, f-VAE is more lightweight and converges faster, achieving the same performance under smaller-size architecture. Recently, deep generative models has been widely studied and developed. Outside of Generative Adversarial Networks (GANs) (Goodfellow et al. 2014), Variational Autoencoders (VAEs) (Kingma and Welling 2013) and flow-based models (Dinh, Krueger, and Bengio 2014; Dinh, Sohldickstein, and Bengio 2016) are two distinct kinds of competitive generative models. They have their own advantages and disadvantages, and we try to integrate them to a new model.