Researchers introduced FuseReg, a regularization method that mitigates the reconstruction-generation gap in representation autoencoders (RAEs) by regularizing layer fusion. The work addresses the open question of which pretrained visual encoder layers should form the shared latent space used for both reconstruction and diffusion-based image generation.
Representation autoencoders (RAEs) reuse features from a pretrained visual encoder as reconstruction and diffusion latents, integrating strong visual representations into image generation. However, RAEs still need to decide which encoder layers form the shared latent space for the generator and pixe
EU-based hosting, git-push deploys, automatic HTTPS, no cold starts. Free tier with a custom domain — perfect for shipping the agent you just read about.