1 citations · 1 across the 1 of their papers we have counts for
7 papers
Concrete Subspace Learning based Interference Elimination for Multi-task Model Fusion
Anke Tang, Xianglin Luo, Li Shen +5
Merging models fine-tuned from a common, extensively pre-trained large model but specialized for different tasks has been demonstrated as a cheap and scalable strategy to construct…
Aligning Few-Step Diffusion Models with Dense Reward Difference Learning
Ziyi Zhang, Li Shen, Sen Zhang +6
Few-step diffusion models enable efficient high-resolution image synthesis but struggle to align with specific downstream objectives due to limitations of existing reinforcement le…
FusionBench: A Unified Library and Comprehensive Benchmark for Deep Model Fusion
Anke Tang, Li Shen, Yong Luo +5
Deep model fusion is an emerging technique that unifies the predictions or parameters of several deep neural networks into a single better-performing model in a cost-effective and…
Learning from models beyond fine-tuning
Hongling Zheng, Li Shen, Anke Tang +5
Foundation models (FM) have demonstrated remarkable performance across a wide range of tasks (especially in the fields of natural language processing and computer vision), primaril…
Stability and Generalization for Distributed SGDA
Miaoxi Zhu, Yan Sun, Li Shen +2
Minimax optimization is gaining increasing attention in modern machine learning applications. Driven by large-scale models and massive volumes of data collected from edge devices,…
Hi-SAM: Marrying Segment Anything Model for Hierarchical Text Segmentation
Maoyuan Ye, Jing Zhang, Juhua Liu +5
The Segment Anything Model (SAM), a profound vision foundation model pretrained on a large-scale dataset, breaks the boundaries of general segmentation and sparks various downstrea…