2023/03/23 by Troy Luhman, Luhman, Troy, Eric Luhman +1
Computer Science · #Generative Adversarial Networks and Image Synthesis #Advanced Image and Video Retrieval Techniques #Advanced Vision and Imaging
paper · pdf · doi:10.48550/arxiv.2303.13714
We present fast, realistic image generation on high-resolution, multimodal datasets using hierarchical variational autoencoders (VAEs) trained on a deterministic autoencoder's latent space. In this two-stage setup, the autoencoder compresses the image into its semantic features, which are then modeled with a deep VAE. With this method, the VAE avoids modeling the fine-grained details that constitute the majority of the image's code length, allowing it to focus on learning its structural components. We demonstrate the effectiveness of our two-stage approach, achieving a FID of 9.34 on the ImageNet-256 dataset which is comparable to BigGAN. We make our implementation available online.