57 citations · 115 across the 12 of their papers we have counts for
6 papers · 1 filter
DACR: Distribution-Augmented Contrastive Reconstruction for Time-Series Anomaly Detection
Lixu Wang, Shichao Xu, Xinyu Du +1
Anomaly detection in time-series data is crucial for identifying faults, failures, threats, and outliers across a range of applications. Recently, deep learning techniques have bee…
Federated Class-Incremental Learning
Jiahua Dong, Lixu Wang, Zhen Fang +4
Federated learning (FL) has attracted growing attention via data-private collaborative training on decentralized clients. However, most existing methods unrealistically assume obje…
Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Authorization
Lixu Wang, Shichao Xu, Ruiqi Xu +2
As Artificial Intelligence as a Service gains popularity, protecting well-trained models as intellectual property is becoming increasingly important. There are two common types of…
Weak Adaptation Learning -- Addressing Cross-domain Data Insufficiency with Weak Annotator
Shichao Xu, Lixu Wang, Yixuan Wang +1
Data quantity and quality are crucial factors for data-driven learning methods. In some target problem domains, there are not many data samples available, which could significantly…
Addressing Class Imbalance in Federated Learning
Lixu Wang, Shichao Xu, Xiao Wang +1
Federated learning (FL) is a promising approach for training decentralized data located on local client devices while improving efficiency and privacy. However, the distribution an…
Eavesdrop the Composition Proportion of Training Labels in Federated Learning
Lixu Wang, Shichao Xu, Xiao Wang +1
Federated learning (FL) has recently emerged as a new form of collaborative machine learning, where a common model can be learned while keeping all the training data on local devic…