1.2k citations · 3.6k across the 152 of their papers we have counts for
11 papers · 2 filters
How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary Investigation
Zhongyi Han, Guanglin Zhou, Rundong He +7
In machine learning, generalization against distribution shifts -- where deployment conditions diverge from the training scenarios -- is crucial, particularly in fields like climat…
Distilling Out-of-Distribution Robustness from Vision-Language Foundation Models
Andy Zhou, Jindong Wang, Yu-Xiong Wang +1
We propose a conceptually simple and lightweight framework for improving the robustness of vision models through the combination of knowledge distillation and data augmentation. We…
A Recent Survey of Heterogeneous Transfer Learning
Runxue Bao, Yiming Sun, Yuhe Gao +4
The application of transfer learning, leveraging knowledge from source domains to enhance model performance in a target domain, has significantly grown, supporting diverse real-wor…
Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks
Hao Chen, Jindong Wang, Ankit Shah +5
Pre-training on large-scale datasets and then fine-tuning on downstream tasks have become a standard practice in deep learning. However, pre-training data often contain label noise…
DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalization
Wang Lu, Jindong Wang, Xinwei Sun +4
Time series remains one of the most challenging modalities in machine learning research. The out-of-distribution (OOD) detection and generalization on time series tend to suffer du…
Improving Generalization of Adversarial Training via Robust Critical Fine-Tuning
Kaijie Zhu, Jindong Wang, Xixu Hu +2
Deep neural networks are susceptible to adversarial examples, posing a significant security risk in critical applications. Adversarial Training (AT) is a well-established technique…