24 citations · 55 across the 7 of their papers we have counts for
7 papers
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated Active Learning
Jun Zhang, Jue Wang, Huan Li +3
Active learning (AL) reduces human annotation costs for machine learning systems by strategically selecting the most informative unlabeled data for annotation, but performing it in…
Towards Uncovering the Intrinsic Data Structures for Unsupervised Domain Adaptation using Structurally Regularized Deep Clustering
Hui Tang, Xiatian Zhu, Ke Chen +2
Unsupervised domain adaptation (UDA) is to learn classification models that make predictions for unlabeled data on a target domain, given labeled data on a source domain whose dist…
MVLidarNet: Real-Time Multi-Class Scene Understanding for Autonomous Driving Using Multiple Views
Ke Chen, Ryan Oldja, Nikolai Smolyanskiy +5
Autonomous driving requires the inference of actionable information such as detecting and classifying objects, and determining the drivable space. To this end, we present Multi-Vie…
Compositional Few-Shot Recognition with Primitive Discovery and Enhancing
Yixiong Zou, Shanghang Zhang, Ke Chen +3
Few-shot learning (FSL) aims at recognizing novel classes given only few training samples, which still remains a great challenge for deep learning. However, humans can easily recog…
Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering
Hui Tang, Ke Chen, Kui Jia
Unsupervised domain adaptation (UDA) is to make predictions for unlabeled data on a target domain, given labeled data on a source domain whose distribution shifts from the target o…
Cascading Convolutional Color Constancy
Huanglin Yu, Ke Chen, Kaiqi Wang +3
Regressing the illumination of a scene from the representations of object appearances is popularly adopted in computational color constancy. However, it's still challenging due to…