15 citations · 57 across the 21 of their papers we have counts for
4 papers · 1 filter
Fast Trainable Projection for Robust Fine-Tuning
Junjiao Tian, Yen-Cheng Liu, James Seale Smith +1
Robust fine-tuning aims to achieve competitive in-distribution (ID) performance while maintaining the out-of-distribution (OOD) robustness of a pre-trained model when transferring…
Diffuse, Attend, and Segment: Unsupervised Zero-Shot Segmentation using Stable Diffusion
Junjiao Tian, Lavisha Aggarwal, Andrea Colaco +2
Producing quality segmentation masks for images is a fundamental problem in computer vision. Recent research has explored large-scale supervised training to enable zero-shot segmen…
Continual Adaptation of Vision Transformers for Federated Learning
Shaunak Halbe, James Seale Smith, Junjiao Tian +1
In this paper, we focus on the important yet understudied problem of Continual Federated Learning (CFL), where a server communicates with a set of clients to incrementally learn ne…
Trainable Projected Gradient Method for Robust Fine-tuning
Junjiao Tian, Xiaoliang Dai, Chih-Yao Ma +3
Recent studies on transfer learning have shown that selectively fine-tuning a subset of layers or customizing different learning rates for each layer can greatly improve robustness…