3.2k citations · 5.9k across the 33 of their papers we have counts for
8 papers · 1 filter
Domain Attention Consistency for Multi-Source Domain Adaptation
Zhongying Deng, Kaiyang Zhou, Yongxin Yang +1
Most existing multi-source domain adaptation (MSDA) methods minimize the distance between multiple source-target domain pairs via feature distribution alignment, an approach borrow…
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…
Energy-Based Open-World Uncertainty Modeling for Confidence Calibration
Yezhen Wang, Bo Li, Tong Che +3
Confidence calibration is of great importance to the reliability of decisions made by machine learning systems. However, discriminative classifiers based on deep neural networks ar…
MixStyle Neural Networks for Domain Generalization and Adaptation
Kaiyang Zhou, Yongxin Yang, Yu Qiao +1
Neural networks do not generalize well to unseen data with domain shifts -- a longstanding problem in machine learning and AI. To overcome the problem, we propose MixStyle, a simpl…
Semi-Supervised Domain Generalization with Stochastic StyleMatch
Kaiyang Zhou, Chen Change Loy, Ziwei Liu
Ideally, visual learning algorithms should be generalizable, for dealing with any unseen domain shift when deployed in a new target environment; and data-efficient, for reducing de…
Domain Generalization with MixStyle
Kaiyang Zhou, Yongxin Yang, Yu Qiao +1
Though convolutional neural networks (CNNs) have demonstrated remarkable ability in learning discriminative features, they often generalize poorly to unseen domains. Domain general…
Domain Generalization: A Survey
Kaiyang Zhou, Ziwei Liu, Yu Qiao +2
Generalization to out-of-distribution (OOD) data is a capability natural to humans yet challenging for machines to reproduce. This is because most learning algorithms strongly rely…