147 citations · 437 across the 16 of their papers we have counts for
22 papers
Variational Model Perturbation for Source-Free Domain Adaptation
Mengmeng Jing, Xiantong Zhen, Jingjing Li +1
We aim for source-free domain adaptation, where the task is to deploy a model pre-trained on source domains to target domains. The challenges stem from the distribution shift from…
Federated Zero-Shot Learning for Visual Recognition
Zhi Chen, Yadan Luo, Sen Wang +2
Zero-shot learning is a learning regime that recognizes unseen classes by generalizing the visual-semantic relationship learned from the seen classes. To obtain an effective ZSL mo…
Spectrum Gaussian Processes Based On Tunable Basis Functions
Wenqi Fang, Guanlin Wu, Jingjing Li +3
Spectral approximation and variational inducing learning for the Gaussian process are two popular methods to reduce computational complexity. However, in previous research, those m…
Exploiting Cross-Session Information for Session-based Recommendation with Graph Neural Networks
Ruihong Qiu, Zi Huang, Jingjing Li +1
Different from the traditional recommender system, the session-based recommender system introduces the concept of the session, i.e., a sequence of interactions between a user and m…
Mitigating Generation Shifts for Generalized Zero-Shot Learning
Zhi Chen, Yadan Luo, Sen Wang +3
Generalized Zero-Shot Learning (GZSL) is the task of leveraging semantic information (e.g., attributes) to recognize the seen and unseen samples, where unseen classes are not obser…
Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain Adaptation
Zhekai Du, Jingjing Li, Hongzu Su +2
Unsupervised Domain Adaptation (UDA) aims to generalize the knowledge learned from a well-labeled source domain to an unlabeled target domain. Recently, adversarial domain adaptati…