Generative Latent Flow: A Framework for Non-adversarial Image Generation

By Zhisheng Xiao, Qing Yan, Yi-An Chen, Yali Amit. 2019

15 citations recorded by the source corpus.

Generative Adversarial Networks (GANs) have been shown to outperform non-adversarial generative models in terms of the image generation quality by a large margin. Recently, researchers have looked into improving non-adversarial alternatives that can close the gap of generation quality while avoiding some common issues of GANs, such as unstable training and mode collapse. Examples in this direction include Two-stage VAE and Generative Latent Nearest Neighbors. However, a major drawback of these models is that they are slow to train, and in particular, they require two training stages. To address this, we propose Generative Latent Flow (GLF), which uses an auto-encoder to learn the mapping to and from the latent space, and an invertible flow to map the distribution in the latent space to simple i.i.d noise. The advantages of our method include a simple conceptual framework, single stage training and fast convergence. Quantitatively, the generation quality of our model significantly outperforms that of VAEs, and is competitive with GANs' benchmark on commonly used datasets.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Generative Models

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Citations in the corpus

Taming Transformers for High-Resolution Image Synthesis (2020, methodology): “[17, 68] demonstrate similar gains when using an unconditional normalizing flow for the second stage, and [55, 56] when using a conditional normalizing flow.”
VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models (2020, background): “Previous work in this direction learns the latent structure of pre-trained VAEs (Dai & Wipf, 2019; Xiao et al., 2019; Ghosh et al., 2020), and sampling from learned latent distributions improves sample quality.”
Network-to-Network Translation with Conditional Invertible Neural Networks (2020, methodology): “Recent works [76, 22] also utilize INNs to transform the latent distribution of an autoencoder to the base distribution.”