48 citations · 105 across the 11 of their papers we have counts for
11 papers
Consistency-Guided Temperature Scaling Using Style and Content Information for Out-of-Domain Calibration
Wonjeong Choi, Jungwuk Park, Dong-Jun Han +2
Research interests in the robustness of deep neural networks against domain shifts have been rapidly increasing in recent years. Most existing works, however, focus on improving th…
NEO-KD: Knowledge-Distillation-Based Adversarial Training for Robust Multi-Exit Neural Networks
Seokil Ham, Jungwuk Park, Dong-Jun Han +1
While multi-exit neural networks are regarded as a promising solution for making efficient inference via early exits, combating adversarial attacks remains a challenging problem. I…
StableFDG: Style and Attention Based Learning for Federated Domain Generalization
Jungwuk Park, Dong-Jun Han, Jinho Kim +3
Traditional federated learning (FL) algorithms operate under the assumption that the data distributions at training (source domains) and testing (target domain) are the same. The f…
Test-Time Style Shifting: Handling Arbitrary Styles in Domain Generalization
Jungwuk Park, Dong-Jun Han, Soyeong Kim +1
In domain generalization (DG), the target domain is unknown when the model is being trained, and the trained model should successfully work on an arbitrary (and possibly unseen) ta…
SplitGP: Achieving Both Generalization and Personalization in Federated Learning
Dong-Jun Han, Do-Yeon Kim, Minseok Choi +2
A fundamental challenge to providing edge-AI services is the need for a machine learning (ML) model that achieves personalization (i.e., to individual clients) and generalization (…
Communication-Computation Efficient Secure Aggregation for Federated Learning
Beongjun Choi, Jy-yong Sohn, Dong-Jun Han +1
Federated learning has been spotlighted as a way to train neural networks using distributed data with no need for individual nodes to share data. Unfortunately, it has also been sh…