Sangheum Hwang

According to our database, Sangheum Hwang authored at least 37 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Context-aware cross feature attentive network for click-through rate predictions.

Soojin Lee, Sangheum Hwang

Appl. Intell., October, 2024

StochCA: A novel approach for exploiting pretrained models with cross-attention.

Seungwon Seo, Suho Lee, Sangheum Hwang

Neural Networks, 2024

Generalized Outlier Exposure: Towards a trustworthy out-of-distribution detector without sacrificing accuracy.

Jiin Koo, Sungjoon Choi, Sangheum Hwang

Neurocomputing, 2024

Reflexive Guidance: Improving OoDD in Vision-Language Models via Self-Guided Image-Adaptive Concept Generation.

Seulbi Lee, Jihyo Kim, Sangheum Hwang

CoRR, 2024

GTA: Guided Transfer of Spatial Attention from Object-Centric Representations.

SeokHyun Seo, Jinwoo Hong, Jungwoo Chae, Kyungyul Kim, Sangheum Hwang

CoRR, 2024

Self-Supervised Representation Learning for Basecalling Nanopore Sequencing Data.

Carlos Vintimilla, Sangheum Hwang

IEEE Access, 2024

Comparison of Out-of-Distribution Detection Performance of CLIP-based Fine-Tuning Methods.

Jeonghyeon Kim, Jihyo Kim, Sangheum Hwang

Proceedings of the International Conference on Electronics, Information, and Communication, 2024

2023

A unified benchmark for the unknown detection capability of deep neural networks.

Jihyo Kim, Jiin Koo, Sangheum Hwang

Expert Syst. Appl., November, 2023

Elucidating robust learning with uncertainty-aware corruption pattern estimation.

Jeongeun Park, Seungyoun Shin, Sangheum Hwang, Sungjoon Choi

Pattern Recognit., June, 2023

Rethinking Evaluation Protocols of Visual Representations Learned via Self-supervised Learning.

Jae-Hun Lee, Doyoung Yoon, Byeongmoon Ji, Kyungyul Kim, Sangheum Hwang

CoRR, 2023

Few-shot Fine-tuning is All You Need for Source-free Domain Adaptation.

Suho Lee, Seungwon Seo, Jihyo Kim, Yejin Lee, Sangheum Hwang

CoRR, 2023

Deep Active Learning with Contrastive Learning Under Realistic Data Pool Assumptions.

Jihyo Kim, Jeonghyeon Kim, Sangheum Hwang

CoRR, 2023

2022

A new smart smudge attack using CNN.

Hansub Shin, Sungyong Sim, Hyukyoon Kwon, Sangheum Hwang, Younho Lee

Int. J. Inf. Sec., 2022

2021

Exploiting Global Structure Information to Improve Medical Image Segmentation.

Jaemoon Hwang, Sangheum Hwang

Sensors, 2021

Similarity based Deep Neural Networks.

Seungyeon Lee, Eunji Jo, Sangheum Hwang, Gyeong Bok Jung, Dohyun Kim

Int. J. Fuzzy Log. Intell. Syst., 2021

Elucidating Noisy Data via Uncertainty-Aware Robust Learning.

Jeongeun Park, Seungyoun Shin, Sangheum Hwang, Sungjoon Choi

CoRR, 2021

Supervised Contrastive Embedding for Medical Image Segmentation.

Sangwoo Lee, Yejin Lee, Geongyu Lee, Sangheum Hwang

IEEE Access, 2021

A Unified Defect Pattern Analysis of Wafer Maps Using Density-Based Clustering.

Jaehoon Koo, Sangheum Hwang

IEEE Access, 2021

Self-Knowledge Distillation with Progressive Refinement of Targets.

Kyungyul Kim, Byeongmoon Ji, Doyoung Yoon, Sangheum Hwang

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

2020

Self-Knowledge Distillation: A Simple Way for Better Generalization.

Kyungyul Kim, Byeongmoon Ji, Doyoung Yoon, Sangheum Hwang

CoRR, 2020

Additive Ensemble Neural Networks.

Minyoung Park, Seungyeon Lee, Sangheum Hwang, Dohyun Kim

IEEE Access, 2020

A New Splitting Criterion for Better Interpretable Trees.

Sangheum Hwang, Hyeon Gyu Yeo, Jung-Sik Hong

IEEE Access, 2020

Confidence-Aware Learning for Deep Neural Networks.

Jooyoung Moon, Jihyo Kim, Younghak Shin, Sangheum Hwang

Proceedings of the 37th International Conference on Machine Learning, 2020

2018

A Scalable Feature Based Clustering Algorithm for Sequences with Many Distinct Items.

Sangheum Hwang, Dohyun Kim

Int. J. Fuzzy Log. Intell. Syst., 2018

Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge.

Mitko Veta, Yujing J. Heng, Nikolas Stathonikos, Babak Ehteshami Bejnordi, Francisco Beca, Thomas Wollmann, Karl Rohr, Manan A. Shah, Dayong Wang, Mikaël Rousson, Martin Hedlund, David Tellez, Francesco Ciompi, Erwan Zerhouni, David Lanyi, Matheus Palhares Viana, Vassili Kovalev, Vitali Liauchuk, Hady Ahmady Phoulady, Talha Qaiser, Simon Graham, Nasir M. Rajpoot, Erik Sjöblom, Jesper Molin, Kyunghyun Paeng, Sangheum Hwang, Sunggyun Park, Zhipeng Jia, Eric I-Chao Chang, Yan Xu, Andrew H. Beck, Paul J. van Diest, Josien P. W. Pluim

CoRR, 2018

Robust relevance vector machine for classification with variational inference.

Sangheum Hwang, Myong K. Jeong

Ann. Oper. Res., 2018

2017

A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology.

Kyunghyun Paeng, Sangheum Hwang, Sunggyun Park, Minsoo Kim

Proceedings of the Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, 2017

Accurate Lung Segmentation via Network-Wise Training of Convolutional Networks.

Sangheum Hwang, Sunggyun Park

Proceedings of the Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, 2017

2016

A robust support vector regression with a linear-log concave loss function.

Dohyun Kim, Chungmok Lee, Sangheum Hwang, Myong K. Jeong

J. Oper. Res. Soc., 2016

Collaborative crystal structure prediction.

Sangheum Hwang, Jiho Yoo, Chanhee Lee, Sang Hyun Lee

Expert Syst. Appl., 2016

A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology.

Kyunghyun Paeng, Sangheum Hwang, Sunggyun Park, Minsoo Kim, Seokhwi Kim

CoRR, 2016

Semantic Noise Modeling for Better Representation Learning.

Hyo-Eun Kim, Sangheum Hwang, Kyunghyun Cho

CoRR, 2016

Scale-Invariant Feature Learning using Deconvolutional Neural Networks for Weakly-Supervised Semantic Segmentation.

Hyo-Eun Kim, Sangheum Hwang

CoRR, 2016

Self-Transfer Learning for Fully Weakly Supervised Object Localization.

Sangheum Hwang, Hyo-Eun Kim

CoRR, 2016

Self-Transfer Learning for Weakly Supervised Lesion Localization.

Sangheum Hwang, Hyo-Eun Kim

Proceedings of the Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016, 2016

A novel approach for tuberculosis screening based on deep convolutional neural networks.

Sangheum Hwang, Hyo-Eun Kim, Jihoon Jeong, Hee-Jin Kim

Proceedings of the Medical Imaging 2016: Computer-Aided Diagnosis, San Diego, 2016

2015

Robust kernel-based regression with bounded influence for outliers.

Sangheum Hwang, Dohyun Kim, Myong K. Jeong, Bong-Jin Yum

J. Oper. Res. Soc., 2015