1 citations · 1 across the 6 of their papers we have counts for
6 papers
Collaborative Learning with Multiple Foundation Models for Source-Free Domain Adaptation
Huisoo Lee, Jisu Han, Hyunsouk Cho +1
Source-Free Domain Adaptation (SFDA) aims to adapt a pre-trained source model to an unlabeled target domain without access to source data. Recent advances in Foundation Models (FMs…
D-TPT: Dimensional Entropy Maximization for Calibrating Test-Time Prompt Tuning in Vision-Language Models
Jisu Han, Wonjun Hwang
Test-time adaptation paradigm provides flexibility towards domain shifts by performing immediate adaptation on unlabeled target data from the source model. Vision-Language Models (…
When Test-Time Adaptation Meets Self-Supervised Models
Jisu Han, Jihee Park, Dongyoon Han +1
Training on test-time data enables deep learning models to adapt to dynamic environmental changes, enhancing their practical applicability. Online adaptation from source to target…
Ranked Entropy Minimization for Continual Test-Time Adaptation
Jisu Han, Jaemin Na, Wonjun Hwang
Test-time adaptation aims to adapt to realistic environments in an online manner by learning during test time. Entropy minimization has emerged as a principal strategy for test-tim…
Semantic Prompting with Image-Token for Continual Learning
Jisu Han, Jaemin Na, Wonjun Hwang
Continual learning aims to refine model parameters for new tasks while retaining knowledge from previous tasks. Recently, prompt-based learning has emerged to leverage pre-trained…
SRIL: Selective Regularization for Class-Incremental Learning
Jisu Han, Jaemin Na, Wonjun Hwang
Human intelligence gradually accepts new information and accumulates knowledge throughout the lifespan. However, deep learning models suffer from a catastrophic forgetting phenomen…