5 citations · 8 across the 5 of their papers we have counts for
5 papers
CANDI: Curated Test-Time Adaptation for Multivariate Time-Series Anomaly Detection Under Distribution Shift
HyunGi Kim, Jisoo Mok, Hyungyu Lee +2
Multivariate time-series anomaly detection (MTSAD) aims to identify deviations from normality in multivariate time-series and is critical in real-world applications. However, in re…
RePIC: Reinforced Post-Training for Personalizing Multi-Modal Language Models
Yeongtak Oh, Dohyun Chung, Juhyeon Shin +4
Recent multi-modal large language models (MLLMs) often struggle to generate personalized image captions, even when trained on high-quality captions. In this work, we observe that s…
SF(DA): Source-free Domain Adaptation Through the Lens of Data Augmentation
Uiwon Hwang, Jonghyun Lee, Juhyeon Shin +1
In the face of the deep learning model's vulnerability to domain shift, source-free domain adaptation (SFDA) methods have been proposed to adapt models to new, unseen target domain…
Entropy is not Enough for Test-Time Adaptation: From the Perspective of Disentangled Factors
Jonghyun Lee, Dahuin Jung, Saehyung Lee +4
Test-time adaptation (TTA) fine-tunes pre-trained deep neural networks for unseen test data. The primary challenge of TTA is limited access to the entire test dataset during online…
STAG: Structural Test-time Alignment of Gradients for Online Adaptation
Juhyeon Shin, Yujin Oh, Jonghyun Lee +5
Test-Time Adaptation (TTA) adapts pre-trained models using only unlabeled test streams, requiring real-time inference and update without access to source data. We propose Structura…