4 citations · 7 across the 10 of their papers we have counts for
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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…
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…
Label-Noise Robust Diffusion Models
Byeonghu Na, Yeongmin Kim, HeeSun Bae +4
Conditional diffusion models have shown remarkable performance in various generative tasks, but training them requires large-scale datasets that often contain noise in conditional…
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…