CutDiffusion: A Simple, Fast, Cheap, and Strong Diffusion Extrapolation Method

1 code implementation • 23 Apr 2024 • Mingbao Lin, Zhihang Lin, Wengyi Zhan, Liujuan Cao, Rongrong Ji

Transforming large pre-trained low-resolution diffusion models to cater to higher-resolution demands, i. e., diffusion extrapolation, significantly improves diffusion adaptability.

Denoising

ReMax: A Simple, Effective, and Efficient Reinforcement Learning Method for Aligning Large Language Models

1 code implementation • 16 Oct 2023 • Ziniu Li, Tian Xu, Yushun Zhang, Zhihang Lin, Yang Yu, Ruoyu Sun, Zhi-Quan Luo

Based on these properties, we develop ReMax, a new algorithm tailored for RLHF.

SMMix: Self-Motivated Image Mixing for Vision Transformers

1 code implementation • ICCV 2023 • Mengzhao Chen, Mingbao Lin, Zhihang Lin, Yuxin Zhang, Fei Chao, Rongrong Ji

Due to the subtle designs of the self-motivated paradigm, our SMMix is significant in its smaller training overhead and better performance than other CutMix variants.

Learning Best Combination for Efficient N:M Sparsity

1 code implementation • 14 Jun 2022 • Yuxin Zhang, Mingbao Lin, Zhihang Lin, Yiting Luo, Ke Li, Fei Chao, Yongjian Wu, Rongrong Ji

In this paper, we show that the N:M learning can be naturally characterized as a combinatorial problem which searches for the best combination candidate within a finite collection.