24 citations · 55 across the 7 of their papers we have counts for
4 papers · 1 filter
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…