High Fidelity Image Synthesis With Deep VAEs In Latent Space

• URL: http://arxiv.org/abs/2303.13714v1
• Date: Thu, 23 Mar 2023 23:45:19 GMT
• Title: High Fidelity Image Synthesis With Deep VAEs In Latent Space
• Authors: Troy Luhman, Eric Luhman
• Abstract summary: We present fast, realistic image generation on high-resolution, multimodal datasets using hierarchical variational autoencoders (VAEs) In this two-stage setup, the autoencoder compresses the image into its semantic features, which are then modeled with a deep VAE. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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