3.2k citations · 3.9k across the 36 of their papers we have counts for
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Generalized Out-of-Distribution Detection: A Survey
Jingkang Yang, Kaiyang Zhou, Yixuan Li +1
Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of machine learning systems. For instance, in autonomous driving, we would like the driving s…
Learning to Prompt for Vision-Language Models
Kaiyang Zhou, Jingkang Yang, Chen Change Loy +1
Large pre-trained vision-language models like CLIP have shown great potential in learning representations that are transferable across a wide range of downstream tasks. Different f…
Semantically Coherent Out-of-Distribution Detection
Jingkang Yang, Haoqi Wang, Litong Feng +4
Current out-of-distribution (OOD) detection benchmarks are commonly built by defining one dataset as in-distribution (ID) and all others as OOD. However, these benchmarks unfortuna…
Progressive Representative Labeling for Deep Semi-Supervised Learning
Xiaopeng Yan, Riquan Chen, Litong Feng +3
Deep semi-supervised learning (SSL) has experienced significant attention in recent years, to leverage a huge amount of unlabeled data to improve the performance of deep learning w…
GIST: Distributed Training for Large-Scale Graph Convolutional Networks
Cameron R. Wolfe, Jingkang Yang, Arindam Chowdhury +4
The graph convolutional network (GCN) is a go-to solution for machine learning on graphs, but its training is notoriously difficult to scale both in terms of graph size and the num…