4 papers
Proactive Gradient Conflict Mitigation in Multi-Task Learning: A Sparse Training Perspective
Zhi Zhang, Jiayi Shen, Congfeng Cao +5
Advancing towards generalist agents necessitates the concurrent processing of multiple tasks using a unified model, thereby underscoring the growing significance of simultaneous mo…
A Dataset and Benchmark for Copyright Infringement Unlearning from Text-to-Image Diffusion Models
Rui Ma, Qiang Zhou, Yizhu Jin +11
Copyright law confers upon creators the exclusive rights to reproduce, distribute, and monetize their creative works. However, recent progress in text-to-image generation has intro…
Gradient-based Parameter Selection for Efficient Fine-Tuning
Zhi Zhang, Qizhe Zhang, Zijun Gao +4
With the growing size of pre-trained models, full fine-tuning and storing all the parameters for various downstream tasks is costly and infeasible. In this paper, we propose a new…
Incremental Residual Concept Bottleneck Models
Chenming Shang, Shiji Zhou, Hengyuan Zhang +3
Concept Bottleneck Models (CBMs) map the black-box visual representations extracted by deep neural networks onto a set of interpretable concepts and use the concepts to make predic…