Home - Liu Yang

Research Interests

Transfer learning

In data mining applications, the lack of labeled data makes supervised learning algorithms fail to build accurate classification models. Transfer learning has been developed to deal with such lack of label problem. It aims to improve the performance of learning by transferring knowledge from several source domains to a target domain. For example, image classification can be modeled as a target learning task where there are only a few labeled training images. Fortunately, it is possible to collect some texts related to images, such as image annotations or documents around images, so that the knowledge from text data (a source domain) can be transferred to classify images in a target domain.

(a) Homogeneous transfer learning

(b) Heterogeneous transfer learning

Federated learning

Federated learning is a machine learning framework that can effectively help multiple institutions to perform data usage and machine learning modeling under the requirements of user privacy protection, data security, and government regulations. Federated learning, as a distributed machine learning paradigm, can effectively solve the problem of data islands, allowing participants to jointly model on the basis of not sharing data, which can technically break data islands and achieve AI collaboration.

Multi-view learning

In real-world applications, examples are described by different feature sets or different “views” due to the innate properties, or collecting from different sources. For instance, in multimedia content understanding, the multimedia segments can be simultaneously described by their video signals from visual camera and audio signals from voice recorder devices. The different views usually contain complementary information, and multi-view learning can exploit this information to learn representation that is more expressive than that of single-view learning methods.

Multi-label learning

The explosive growth of online content such as images and videos nowadays has made developing classification system a very challenging problem. Such new classification system is usually required to assign multiple labels to one single instance: an image might be annotated by many semantic tags in image classification; one article can focus on several topics for text mining. Most of the conventional classification techniques under the assumption that an object only refers to one single class fail to work in such scenario. Therefore, methods that are capable of accomplishing multi-label learning can be more and more important.

Publications

Haodong Zhang, Liu Yang , Qinghua Hu, Liping Jing. Federated Continual Learning Based on Prototype Learning (in Chinese). Sci Sin Inform, 2024. (CCF-A类)
Yuting Liu, Liu Yang , Yu Wang. Hierarchical Fine-grained Visual Classification Leveraging Consistent Hierarchical Knowledge. European Conference on Machine Learning, 2024. (CCF-B类)
Liu Yang , Shiqiao Gu, Chenyang Shen, Xile Zhao, Qinghua Hu. Soft Independence Guided Filter Pruning, Pattern Recognition, 2024. (SCI一区)
Siqi Deng, Liu Yang . Enhancing Consistent Federated Learning Objectives through Uniform Feature Distributions, IEEE International Conference on Multimedia and Expo , 2024. (CCF-B类)
Jing Li, Liu Yang* , Qinghua Hu. Enhancing Multi-Source Open-Set Domain Adaptation through Nearest Neighbor Classification with Self-Supervised Vision Transforme, IEEE Transactions on Circuits and Systems for Video Technology , 2024.(SCI一区)
Qilong Wang, Yiwen Wu, Liu Yang , Wangmeng Zuo, Qinghua Hu. Layer-Specific Knowledge Distillation for Class Incremental Semantic Segmentation, IEEE Transactions on Image Processing , 2024.(CCF-A类)
Zixuan Qin, Liu Yang* , Qilong Wang, Qinghua Hu. Reliable and Interpretable Personalized Federated Learning, IEEE Conference on Computer Vision and Pattern Recognition , 2023.(CCF-A类)
Jing Li, Liu Yang* , Qilong Wang, Qinghua Hu. WDAN: A Weighted Discriminative Adversarial Network with Dual Classifiers for Fine-Grained Open-Set Domain Adaptation, IEEE Transactions on Circuits and Systems for Video Technology , 2023.(SCI一区)
Jing Li, Liu Yang* , Qilong Wang, Qinghua Hu. Corse helps fine: A Multi-Granularity Discriminative Adversarial Network for Fine-grained Open Set Domain Adaptation. IEEE International Conference on Multi-media and Expo , 2023.(CCF-B类)
Zixuan Qin, Liu Yang* , Fei Gao, Qinghua Hu, Chenyang Shen. Uncertainty-Aware Aggregation for Federated Open Set Domain Adaptation, IEEE Transactions on Neural Networks and Learning Systems , 2022.(SCI一区)
Yikang Wei, Liu Yang* , Yahong Han, Qinghua Hu. Multi-source Collaborative Contrastive Learning for Decentralized Domain Adaptation. IEEE Transactions on Circuits and Systems for Video Technology , 2022.(SCI一区)
Liu Yang , Chenyang Shen, Qinghua Hu*, Liping Jing, Yingbo Li. Adaptive Sample-Level Graph Combination for Partial Multiview Clustering, IEEE Transactions on Image Processing , Accept.(CCF-A类)
Lei Zhang, Yueqiang Zhang, Beibei Wang, Xiaolong Zheng, Liu Yang* . WiCrowd: Counting the Directional Crowd with A Single Wireless Link, IEEE Internet of Things Journal , 2020, Accepted.(SCI一区)
Liu Yang ; Li Maoying; Shen Chenyang; Hu Qinghua*; Wen Jia; Xu Shujie; Discriminative Transfer Learning for Driving Patterns Recognition in Unlabeled Scenes, IEEE Transactions on Cybernetics , 2020, Accepted.(SCI一区)
Hui Li, Liu Yang* , and Fei Gao. More Attentional Local Descriptors for Few-Shot Learning. International Conference on Artificial Neural Networks . Springer, Cham, 2020.(CCF-C类)
YaNing Li, Liu Yang* . More Correlations Better Performance: Fully Associative Networks for Multi-label Image Classification. International Conference on Pattern Recognition , 2020.(CCF-C类)
Jia Wen, Liu Yang* , Chenyang Shen, Fast and Robust Compression of Deep Convolutional Neural Networks, International Conference on Artificial Neural Networks , 2020.(CCF-C类)
Yafang Li, Caiyan Jia, Xiangnan Kong, Liu Yang , and Jian Yu. Locally weighted fusion of structural and attribute information in graph clustering. IEEE Transactions on Cybernetics , 49(1): 247-260, 2019.(SCI一区)
Yao Tan, Liu Yang* , Qinghua Hu, Zhibin Du. Batch Mode Active Learning for Semantic Segmentation Based on Multi-Clue Sample Selection, in Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM) , 2019. November 3-7, 2019 China National Convention Center, Beijing, China(CCF-B类)
Lei Zhang, Zhirui Wang, Liu Yang* . Commercial Wi-Fi Based Fall Detection with Environment Influence Mitigation, in Proceedings of International Conference on Sensing, Communication and Networking, (SECON), 2019.(CCF-B类)
Jian Xu, Xinyue Wang, Zixin Cai, Liu Yang , Liping Jing. Informative Instance Detection for Active Learning on Imbalanced Data, International Joint Conference on Neural Networks, (IJCNN), Budapest Hungary, 14-19 July 2019.(CCF-C类)
Pengfei Zhu, Ren Qi, Qinghua Hu, Qilong Wang, Changqing Zhang and Liu Yang , Beyond similar and dissimilar relations: A kernel regression formulation for metric learning, in Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), 2018, 3242-3248.17.(CCF-A类)
Bangjun Wang, Liu Yang , Li Zhang, and Fanzhang Li, Robust Multi-view Features Fusion Method Based on CNMF, in Proceedings of the 25th International Conference on Neural Information Processing (ICONIP), 2018, 27-39.(CCF-C类)
Liu Yang , Jian Yu, Ye Liu, Dechuan Zhan. Research Progress on the Cognitive Oriented Multi-Source Data Learning Theory and Algorithm, Journal of Software, 28(11):2971-2991, 2017.(CCF-中文A类)
Liping Jing, Chenyang Shen, Liu Yang , Jian Yu, and Michael K. Ng. Multi-Label Classification by Semi-Supervised Singular Value Decomposition. IEEE Transactions on Image Processing , 26(10): 4612-4625, 2017.(CCF-A类)
Liu Yang , Liping Jing, and Jian Yu. Common latent space identification for heterogeneous co-transfer clustering, Neurocomputing, 269: 29-39, 2017.(SCI二区)
Liu Yang , Liping Jing, Jian Yu, and Michael K. Ng. Learning transferred weights from co-occurrence data for heterogeneous transfer learning, IEEE Transactions on Neural Networks and Learning Systems , 27(11): 2187-2200, 2016.(SCI一区)
Liu Yang , Liping Jing, Michael K. Ng, and Jian Yu. A discriminative and sparse topic model for image classification and annotation, Image and Vision Computing, 51: 22-35, 2016.(SCI三区)
Liu Yang , Liping Jing, and Jian Yu. Common latent space identification for heterogeneous co-transfer clustering, in the 2015 International Conference of Intelligence Science and Big Data Engineering (ISCIDE), 2015, 395-406.
Liu Yang , Liping Jing, and Michael K. Ng. Robust and non-negative collective matrix factorization for text-to-image transfer learning, IEEE Transactions on Image Processing , 24(12): 4701-4714, 2015.(CCF-A类)
Liu Yang , Liping Jing, Jian Yu. A transductive heterogeneous transfer learning algorithm, Journal of Software, 26(11): 2762-2780, 2015.(CCF-中文A类)
Liu Yang , Jian Yu, Liping Jing. An adaptive large margin nearest neighbor classification algorithm, Journal of Computer Research and Development, 50(11): 2269-2277, 2013.(CCF-中文A类)
Liping Jing, Liu Yang , Jian Yu, Michael K. Ng. Semi-supervised low-rank mapping learning for multi-label classification, in the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, 1483-1491.(CCF-A类)
Liping Jing, Peng Wang, Liu Yang . Sparse probabilistic matrix factorization by Laplace distribution for collaborative filtering, in Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI), 2015, 1771-1777.(CCF-A类)
Liu Yang , Liping Jing, and Jian Yu. Heterogeneous co-transfer spectral clustering, in the 9th International Conference on Rough Sets and Knowledge Technology (RSKT), 2014, 352-363.