2 citations · 2 across the 3 of their papers we have counts for
4 papers
Exploiting Local Flatness for Efficient Out-of-Distribution Detection
Seonghwan Park, Hyunji Jung, Dongyeop Lee +1
Detecting out-of-distribution (OOD) data is crucial for reliable machine learning deployment. Among detection strategies, post-hoc methods are particularly attractive due to their…
An Analysis of Concept Bottleneck Models: Measuring, Understanding, and Mitigating the Impact of Noisy Annotations
Seonghwan Park, Jueun Mun, Donghyun Oh +1
Concept bottleneck models (CBMs) ensure interpretability by decomposing predictions into human interpretable concepts. Yet the annotations used for training CBMs that enable this t…
ZIP: An Efficient Zeroth-order Prompt Tuning for Black-box Vision-Language Models
Seonghwan Park, Jaehyeon Jeong, Yongjun Kim +2
Recent studies have introduced various approaches for prompt-tuning black-box vision-language models, referred to as black-box prompt-tuning (BBPT). While BBPT has demonstrated con…
FedFwd: Federated Learning without Backpropagation
Seonghwan Park, Dahun Shin, Jinseok Chung +1
In federated learning (FL), clients with limited resources can disrupt the training efficiency. A potential solution to this problem is to leverage a new learning procedure that do…