98 citations · 213 across the 18 of their papers we have counts for
7 papers · 1 filter
FIXED: Frustratingly Easy Domain Generalization with Mixup
Wang Lu, Jindong Wang, Han Yu +4
Domain generalization (DG) aims to learn a generalizable model from multiple training domains such that it can perform well on unseen target domains. A popular strategy is to augme…
Out-of-Distribution Representation Learning for Time Series Classification
Wang Lu, Jindong Wang, Xinwei Sun +2
Time series classification is an important problem in real world. Due to its non-stationary property that the distribution changes over time, it remains challenging to build models…
Towards Optimization and Model Selection for Domain Generalization: A Mixup-guided Solution
Wang Lu, Jindong Wang, Yidong Wang +1
The distribution shifts between training and test data typically undermine the performance of models. In recent years, lots of work pays attention to domain generalization (DG) whe…
Domain Generalization for Activity Recognition via Adaptive Feature Fusion
Xin Qin, Jindong Wang, Yiqiang Chen +2
Human activity recognition requires the efforts to build a generalizable model using the training datasets with the hope to achieve good performance in test datasets. However, in r…
Domain-invariant Feature Exploration for Domain Generalization
Wang Lu, Jindong Wang, Haoliang Li +2
Deep learning has achieved great success in the past few years. However, the performance of deep learning is likely to impede in face of non-IID situations. Domain generalization (…
Semantic-Discriminative Mixup for Generalizable Sensor-based Cross-domain Activity Recognition
Wang Lu, Jindong Wang, Yiqiang Chen +3
It is expensive and time-consuming to collect sufficient labeled data to build human activity recognition (HAR) models. Training on existing data often makes the model biased towar…
MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized Healthcare
Yiqiang Chen, Wang Lu, Xin Qin +2
Federated learning has attracted increasing attention to building models without accessing the raw user data, especially in healthcare. In real applications, different federations…