15 citations · 25 across the 7 of their papers we have counts for
7 papers
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…
RoPAWS: Robust Semi-supervised Representation Learning from Uncurated Data
Sangwoo Mo, Jong-Chyi Su, Chih-Yao Ma +4
Semi-supervised learning aims to train a model using limited labels. State-of-the-art semi-supervised methods for image classification such as PAWS rely on self-supervised represen…
When does the student surpass the teacher? Federated Semi-supervised Learning with Teacher-Student EMA
Jessica Zhao, Sayan Ghosh, Akash Bharadwaj +1
Semi-Supervised Learning (SSL) has received extensive attention in the domain of computer vision, leading to development of promising approaches such as FixMatch. In scenarios wher…
Structure-Encoding Auxiliary Tasks for Improved Visual Representation in Vision-and-Language Navigation
Chia-Wen Kuo, Chih-Yao Ma, Judy Hoffman +1
In Vision-and-Language Navigation (VLN), researchers typically take an image encoder pre-trained on ImageNet without fine-tuning on the environments that the agent will be trained…
Polyhistor: Parameter-Efficient Multi-Task Adaptation for Dense Vision Tasks
Yen-Cheng Liu, Chih-Yao Ma, Junjiao Tian +2
Adapting large-scale pretrained models to various downstream tasks via fine-tuning is a standard method in machine learning. Recently, parameter-efficient fine-tuning methods show…
Open-Set Semi-Supervised Object Detection
Yen-Cheng Liu, Chih-Yao Ma, Xiaoliang Dai +4
Recent developments for Semi-Supervised Object Detection (SSOD) have shown the promise of leveraging unlabeled data to improve an object detector. However, thus far these methods h…
Unbiased Teacher v2: Semi-supervised Object Detection for Anchor-free and Anchor-based Detectors
Yen-Cheng Liu, Chih-Yao Ma, Zsolt Kira
With the recent development of Semi-Supervised Object Detection (SS-OD) techniques, object detectors can be improved by using a limited amount of labeled data and abundant unlabele…