420 citations · 1.6k across the 44 of their papers we have counts for
6 papers · 1 filter
Cross-domain Contrastive Learning for Unsupervised Domain Adaptation
Rui Wang, Zuxuan Wu, Zejia Weng +3
Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a fully-labeled source domain to a different unlabeled target domain. Most existing UDA methods learn d…
Self-Supervised Graph Representation Learning via Topology Transformations
Xiang Gao, Wei Hu, Guo-Jun Qi
We present the Topology Transformation Equivariant Representation learning, a general paradigm of self-supervised learning for node representations of graph data to enable the wide…
Contrastive Learning with Stronger Augmentations
Xiao Wang, Guo-Jun Qi
Representation learning has significantly been developed with the advance of contrastive learning methods. Most of those methods have benefited from various data augmentations that…
Hierarchical Deep CNN Feature Set-Based Representation Learning for Robust Cross-Resolution Face Recognition
Guangwei Gao, Yi Yu, Jian Yang +2
Cross-resolution face recognition (CRFR), which is important in intelligent surveillance and biometric forensics, refers to the problem of matching a low-resolution (LR) probe face…
Self-Supervised Multi-View Learning via Auto-Encoding 3D Transformations
Xiang Gao, Wei Hu, Guo-Jun Qi
3D object representation learning is a fundamental challenge in computer vision to infer about the 3D world. Recent advances in deep learning have shown their efficiency in 3D obje…
A Bayesian Federated Learning Framework with Online Laplace Approximation
Liangxi Liu, Xi Jiang, Feng Zheng +4
Federated learning (FL) allows multiple clients to collaboratively learn a globally shared model through cycles of model aggregation and local model training, without the need to s…