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cs.LG2024★ 1 cited
Unknown Domain Inconsistency Minimization for Domain Generalization
Seungjae Shin, HeeSun Bae, Byeonghu Na +2
The objective of domain generalization (DG) is to enhance the transferability of the model learned from a source domain to unobserved domains. To prevent overfitting to a specific…
cs.LG2024
Dirichlet-based Per-Sample Weighting by Transition Matrix for Noisy Label Learning
HeeSun Bae, Seungjae Shin, Byeonghu Na +1
For learning with noisy labels, the transition matrix, which explicitly models the relation between noisy label distribution and clean label distribution, has been utilized to achi…
cs.CL2024
Make Prompts Adaptable: Bayesian Modeling for Vision-Language Prompt Learning with Data-Dependent Prior
Youngjae Cho, HeeSun Bae, Seungjae Shin +3
Recent Vision-Language Pretrained (VLP) models have become the backbone for many downstream tasks, but they are utilized as frozen model without learning. Prompt learning is a meth…