97 citations · 333 across the 34 of their papers we have counts for
40 papers
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 (…
Locally Supervised Learning with Periodic Global Guidance
Hasnain Irshad Bhatti, Jaekyun Moon
Locally supervised learning aims to train a neural network based on a local estimation of the global loss function at each decoupled module of the network. Auxiliary networks are t…
Task-Adaptive Feature Transformer with Semantic Enrichment for Few-Shot Segmentation
Jun Seo, Young-Hyun Park, Sung Whan Yoon +1
Few-shot learning allows machines to classify novel classes using only a few labeled samples. Recently, few-shot segmentation aiming at semantic segmentation on low sample data has…
GenLabel: Mixup Relabeling using Generative Models
Jy-yong Sohn, Liang Shang, Hongxu Chen +3
Mixup is a data augmentation method that generates new data points by mixing a pair of input data. While mixup generally improves the prediction performance, it sometimes degrades…
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…