2 citations · 4 across the 8 of their papers we have counts for
8 papers
Feature Diversification and Adaptation for Federated Domain Generalization
Seunghan Yang, Seokeon Choi, Hyunsin Park +3
Federated learning, a distributed learning paradigm, utilizes multiple clients to build a robust global model. In real-world applications, local clients often operate within their…
Improving Small Footprint Few-shot Keyword Spotting with Supervision on Auxiliary Data
Seunghan Yang, Byeonggeun Kim, Kyuhong Shim +1
Few-shot keyword spotting (FS-KWS) models usually require large-scale annotated datasets to generalize to unseen target keywords. However, existing KWS datasets are limited in scal…
Label Shift Adapter for Test-Time Adaptation under Covariate and Label Shifts
Sunghyun Park, Seunghan Yang, Jaegul Choo +1
Test-time adaptation (TTA) aims to adapt a pre-trained model to the target domain in a batch-by-batch manner during inference. While label distributions often exhibit imbalances in…
Progressive Random Convolutions for Single Domain Generalization
Seokeon Choi, Debasmit Das, Sungha Choi +3
Single domain generalization aims to train a generalizable model with only one source domain to perform well on arbitrary unseen target domains. Image augmentation based on Random…
Improving Test-Time Adaptation via Shift-agnostic Weight Regularization and Nearest Source Prototypes
Sungha Choi, Seunghan Yang, Seokeon Choi +1
This paper proposes a novel test-time adaptation strategy that adjusts the model pre-trained on the source domain using only unlabeled online data from the target domain to allevia…
Personalized Keyword Spotting through Multi-task Learning
Seunghan Yang, Byeonggeun Kim, Inseop Chung +1
Keyword spotting (KWS) plays an essential role in enabling speech-based user interaction on smart devices, and conventional KWS (C-KWS) approaches have concentrated on detecting us…