Robust kernel-based regression with bounded influence for outliers. - Sangheum Hwang; Dohyun Kim 005; Myong K. Jeong; Bong-Jin Yum (J. Oper. Res. Soc., 2015)

A novel approach for tuberculosis screening based on deep convolutional neural networks. - Sangheum Hwang; Hyo-Eun Kim; Jihoon Jeong; Hee-Jin Kim (Computer-Aided Diagnosis, 2016)

Self-Transfer Learning for Weakly Supervised Lesion Localization. - Sangheum Hwang; Hyo-Eun Kim (MICCAI (2), 2016)

Self-Transfer Learning for Fully Weakly Supervised Object Localization. - Sangheum Hwang; Hyo-Eun Kim (CoRR, 2016)

Scale-Invariant Feature Learning using Deconvolutional Neural Networks for Weakly-Supervised Semantic Segmentation. - Hyo-Eun Kim; Sangheum Hwang (CoRR, 2016)

Semantic Noise Modeling for Better Representation Learning. - Hyo-Eun Kim; Sangheum Hwang; Kyunghyun Cho (CoRR, 2016)

A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology. - Kyunghyun Paeng; Sangheum Hwang; Sunggyun Park; Minsoo Kim; Seokhwi Kim (CoRR, 2016)

Collaborative crystal structure prediction. - Sangheum Hwang; Jiho Yoo; Chanhee Lee; Sang Hyun Lee (Expert Syst. Appl., 2016)

A robust support vector regression with a linear-log concave loss function. - Dohyun Kim 005; Chungmok Lee; Sangheum Hwang; Myong K. Jeong (J. Oper. Res. Soc., 2016)

Accurate Lung Segmentation via Network-Wise Training of Convolutional Networks. - Sangheum Hwang; Sunggyun Park (DLMIA/ML-CDS@MICCAI, 2017)

A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology. - Kyunghyun Paeng; Sangheum Hwang; Sunggyun Park; Minsoo Kim (DLMIA/ML-CDS@MICCAI, 2017)

Robust relevance vector machine for classification with variational inference. - Sangheum Hwang; Myong K. Jeong (Ann. Oper. Res., 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; et al. (CoRR, 2018)

A Scalable Feature Based Clustering Algorithm for Sequences with Many Distinct Items. - Sangheum Hwang; Dohyun Kim 005 (Int. J. Fuzzy Log. Intell. Syst., 2018)

Confidence-Aware Learning for Deep Neural Networks. - Jooyoung Moon; Jihyo Kim; Younghak Shin; Sangheum Hwang (ICML, 2020)

A New Splitting Criterion for Better Interpretable Trees. - Sangheum Hwang; Hyeon Gyu Yeo; Jung-Sik Hong (IEEE Access, 2020)

Additive Ensemble Neural Networks. - Minyoung Park; Seungyeon Lee 002; Sangheum Hwang; Dohyun Kim 005 (IEEE Access, 2020)

Self-Knowledge Distillation: A Simple Way for Better Generalization. - Kyungyul Kim; Byeongmoon Ji; Doyoung Yoon; Sangheum Hwang (CoRR, 2020)

Self-Knowledge Distillation with Progressive Refinement of Targets. - Kyungyul Kim; Byeongmoon Ji; Doyoung Yoon; Sangheum Hwang (ICCV, 2021)

A Unified Defect Pattern Analysis of Wafer Maps Using Density-Based Clustering. - Jaehoon Koo; Sangheum Hwang (IEEE Access, 2021)

Supervised Contrastive Embedding for Medical Image Segmentation. - Sangwoo Lee; Yejin Lee 001; Geongyu Lee; Sangheum Hwang (IEEE Access, 2021)

Elucidating Noisy Data via Uncertainty-Aware Robust Learning. - Jeongeun Park 002; Seungyoun Shin; Sangheum Hwang; Sungjoon Choi (CoRR, 2021)

Similarity based Deep Neural Networks. - Seungyeon Lee 002; Eunji Jo; Sangheum Hwang; Gyeong Bok Jung; Dohyun Kim 005 (Int. J. Fuzzy Log. Intell. Syst., 2021)

Exploiting Global Structure Information to Improve Medical Image Segmentation. - Jaemoon Hwang; Sangheum Hwang (Sensors, 2021)

A new smart smudge attack using CNN. - Hansub Shin; Sungyong Sim; Hyukyoon Kwon; Sangheum Hwang; Younho Lee (Int. J. Inf. Sec., 2022)

Deep Active Learning with Contrastive Learning Under Realistic Data Pool Assumptions. - Jihyo Kim; Jeonghyeon 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 001; Sangheum Hwang (CoRR, 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)

Elucidating robust learning with uncertainty-aware corruption pattern estimation. - Jeongeun Park 002; Seungyoun Shin; Sangheum Hwang; Sungjoon Choi (Pattern Recognit., 2023)

A unified benchmark for the unknown detection capability of deep neural networks. - Jihyo Kim; Jiin Koo; Sangheum Hwang (Expert Syst. Appl., 2023)

Comparison of Out-of-Distribution Detection Performance of CLIP-based Fine-Tuning Methods. - Jeonghyeon Kim; Jihyo Kim; Sangheum Hwang (ICEIC, 2024)

Self-Supervised Representation Learning for Basecalling Nanopore Sequencing Data. - Carlos Vintimilla; Sangheum Hwang (IEEE Access, 2024)

GTA: Guided Transfer of Spatial Attention from Object-Centric Representations. - SeokHyun Seo; Jinwoo Hong; Jungwoo Chae; Kyungyul Kim; Sangheum Hwang (CoRR, 2024)

Generalized Outlier Exposure: Towards a trustworthy out-of-distribution detector without sacrificing accuracy. - Jiin Koo; Sungjoon Choi; Sangheum Hwang (Neurocomputing, 2024)

StochCA: A novel approach for exploiting pretrained models with cross-attention. - Seungwon Seo; Suho Lee; Sangheum Hwang (Neural Networks, 2024)

Context-aware cross feature attentive network for click-through rate predictions. - Soojin Lee; Sangheum Hwang (Appl. Intell., 2024)

APT: Adaptive Personalized Training for Diffusion Models with Limited Data. - Jungwoo Chae; Jiyoon Kim; Jaewoong Choi; Kyungyul Kim; Sangheum Hwang (CVPR, 2025)

Enhanced OoD Detection through Cross-Modal Alignment of Multi-Modal Representations. - Jeonghyeon Kim; Sangheum Hwang (CVPR, 2025)

Reflexive Guidance: Improving OoDD in Vision-Language Models via Self-Guided Image-Adaptive Concept Generation. - Jihyo Kim; Seulbi Lee; Sangheum Hwang (ICLR, 2025)

Parallel Rescaling: Rebalancing Consistency Guidance for Personalized Diffusion Models. - Jungwoo Chae; Jiyoon Kim; Sangheum Hwang (CoRR, 2025)

Metadata Enriched Multi-Instance Contrastive Learning for High-Quality Facial Skin Visual Representations. - Jihyo Kim; Sungchul Kim; Seungwon Seo; Bumsoo Kim; Daejeong Mun; Hoonjae Lee; Sangheum Hwang (Appl. Artif. Intell., 2025)