EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modeling

Understand

Latent generative models have emerged as a leading approach for high-quality image synthesis.

• These models rely on an autoencoder to compress images into a latent space, followed by a generative model to learn the latent distribution.
• We identify that existing autoencoders lack equivariance to semantic-preserving transformations like scaling and rotation, resulting in complex latent spaces that hinder generative performance.
• To address this, we propose EQ-VAE, a simple regularization approach that enforces equivariance in the latent space, reducing its complexity without degrading reconstruction quality.