2 citations · 2 across the 7 of their papers we have counts for
7 papers
Problem-Solving Guide: Predicting the Algorithm Tags and Difficulty for Competitive Programming Problems
Juntae Kim, Eunjung Cho, Dongbin Na
The recent program development industries have required problem-solving abilities for engineers, especially application developers. However, AI-based education systems to help solv…
Pseudo Outlier Exposure for Out-of-Distribution Detection using Pretrained Transformers
Jaeyoung Kim, Kyuheon Jung, Dongbin Na +3
For real-world language applications, detecting an out-of-distribution (OOD) sample is helpful to alert users or reject such unreliable samples. However, modern over-parameterized…
Self-accumulative Vision Transformer for Bone Age Assessment Using the Sauvegrain Method
Hong-Jun Choi, Dongbin Na, Kyungjin Cho +3
This study presents a novel approach to bone age assessment (BAA) using a multi-view, multi-task classification model based on the Sauvegrain method. A straightforward solution to…
Bag of Tricks for In-Distribution Calibration of Pretrained Transformers
Jaeyoung Kim, Dongbin Na, Sungchul Choi +1
While pre-trained language models (PLMs) have become a de-facto standard promoting the accuracy of text classification tasks, recent studies find that PLMs often predict over-confi…
Key Feature Replacement of In-Distribution Samples for Out-of-Distribution Detection
Jaeyoung Kim, Seo Taek Kong, Dongbin Na +1
Out-of-distribution (OOD) detection can be used in deep learning-based applications to reject outlier samples from being unreliably classified by deep neural networks. Learning to…
Unrestricted Black-box Adversarial Attack Using GAN with Limited Queries
Dongbin Na, Sangwoo Ji, Jong Kim
Adversarial examples are inputs intentionally generated for fooling a deep neural network. Recent studies have proposed unrestricted adversarial attacks that are not norm-constrain…
A Neural Pre-Conditioning Active Learning Algorithm to Reduce Label Complexity
Seo Taek Kong, Soomin Jeon, Dongbin Na +3
Deep learning (DL) algorithms rely on massive amounts of labeled data. Semi-supervised learning (SSL) and active learning (AL) aim to reduce this label complexity by leveraging unl…