arXiv Machine Learning
2d ago

Purify Before You Align: Improving Representation Alignment for Diffusion Models

The paper proposes Local Appearance Purification (LAP), a method that refines the alignment target for diffusion models by removing locally predictable content and focusing on the residual. LAP improves generation quality across multiple pretrained encoders, notably reducing FID from 53.1 to 42.3 on ImageNet-256 with MAE. The authors provide a theoretical framework linking alignment effectiveness to the information emphasized by the target and introduce a constant‑readout test to predict purification benefits.

By Yingheng Wang, Yaoqiang Li, Yaqin Wu, Junwen Bai, Jiatao Gu, Christopher De Sa, Volodymyr Kuleshov
arXiv Computer Vision
2d ago

Retrofitting Recurrent Vision-Language Models from Pretrained Backbones

The paper introduces a method to retrofit depth recurrence into pretrained vision‑language models, reducing the language backbone’s parameter count by pruning layers and looping over the remaining ones with linear adapters. A three‑stage recovery training—language‑only pretraining, vision‑language projector alignment, and full fine‑tuning—helps recover performance, with distillation from the non‑recurrent model further improving results. Experiments on TinyLLaVA‑1.4B show the retrofitted recurrent model outperforms the unpruned baseline while saving 23.3% of language parameters and performs better on reasoning‑oriented benchmarks.

By Madhav Iyengar, Andreas Geiger, Hilde K\"uhne
arXiv Computer Vision
Sep 28

FuseReg: Regularizing Layer Fusion Mitigates the Reconstruction-Generation Gap in Representation Autoencoders

arXiv:2609.31620v1 Announce Type: new Abstract: Representation autoencoders (RAEs) reuse features from a pretrained visual encoder as reconstruction and diffusion latents, integrating strong visual r...

By Hongyang Du, Yunfei Xie, Junjie Ye, Jiawei Yang, Xiaoyan Cong, Haodong Zhang, Yongchao Huang, Haiyu Wu, Zongxia Li, Shihang Gui, Dawei Liu, Runhao Li, Jingcheng Ni, Chen Wei, Randall Balestriero, Yue Wang