1 citations · 1 across the 5 of their papers we have counts for
5 papers
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…
Spectral Clustering for Crowdsourcing with Inherently Distinct Task Types
Saptarshi Mandal, Seo Taek Kong, Dimitrios Katselis +1
The Dawid-Skene model is the most widely assumed model in the analysis of crowdsourcing algorithms that estimate ground-truth labels from noisy worker responses. In this work, we a…
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…
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…
Almost Boltzmann Exploration
Harsh Gupta, Seo Taek Kong, R. Srikant +1
Boltzmann exploration is widely used in reinforcement learning to provide a trade-off between exploration and exploitation. Recently, in (Cesa-Bianchi et al., 2017) it has been sho…