103 citations · 137 across the 7 of their papers we have counts for
7 papers
Order-preserving Consistency Regularization for Domain Adaptation and Generalization
Mengmeng Jing, Xiantong Zhen, Jingjing Li +1
Deep learning models fail on cross-domain challenges if the model is oversensitive to domain-specific attributes, e.g., lightning, background, camera angle, etc. To alleviate this…
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…
Alleviating Feature Confusion for Generative Zero-shot Learning
Jingjing Li, Mengmeng Jing, Ke Lu +3
Lately, generative adversarial networks (GANs) have been successfully applied to zero-shot learning (ZSL) and achieved state-of-the-art performance. By synthesizing virtual unseen…
Agile Domain Adaptation
Jingjing Li, Mengmeng Jing, Yue Xie +2
Domain adaptation investigates the problem of leveraging knowledge from a well-labeled source domain to an unlabeled target domain, where the two domains are drawn from different d…
From Zero-Shot Learning to Cold-Start Recommendation
Jingjing Li, Mengmeng Jing, Ke Lu +3
Zero-shot learning (ZSL) and cold-start recommendation (CSR) are two challenging problems in computer vision and recommender system, respectively. In general, they are independentl…
Locality Preserving Joint Transfer for Domain Adaptation
Li Jingjing, Jing Mengmeng, Lu Ke +2
Domain adaptation aims to leverage knowledge from a well-labeled source domain to a poorly-labeled target domain. A majority of existing works transfer the knowledge at either feat…
Leveraging the Invariant Side of Generative Zero-Shot Learning
Jingjing Li, Mengmeng Jin, Ke Lu +3
Conventional zero-shot learning (ZSL) methods generally learn an embedding, e.g., visual-semantic mapping, to handle the unseen visual samples via an indirect manner. In this paper…