FuseReg: Regularizing Layer Fusion Mitigates the Reconstruction-Generation Gap in Representation Autoencoders
Paper recorded by Signals 4 on 2026-09-25 in cs.CV. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-25 on arXiv · recorded by Signals 4 on 2026-09-28
Category: cs.CV · 计算机视觉 · first seen 2026-09-28
Abstract
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 pixel decoder. This choice involves a trade-off. Shallower layers tend to preserve fine pixel details be
Read on arXiv →
Cite this page: FuseReg: Regularizing Layer Fusion Mitigates the Reconstruction-Generation Gap in Representation Autoencoders: the #17 most recent of 342 cs.CV papers we have recorded (as of 2026-09-25). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/fusereg-regularizing-layer-fusion-mitigates-the-reconstruction-generation-gap-in.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable:
papers.json
Get 4 AI signals a day by email — free.
Get 4 AI signals a day by email — free