Publications

[IJ] J. Koo, S. Choi, and S. Hwang (2024), "Generalized Outlier Exposure: Towards a trustworthy out-of-distribution detector without sacrificing accuracy", Neurocomputing, 127371. [paper]

[IC] S. Seo, J. Hong, J. Chae, K. Kim, and S. Hwang, "GTA: Guided Transfer of Spatial Attention from Object-Centric Representations", CVPR 2024 Workshop on Transformers for Vision, 2024.06. [paper]

[IC] J. Kim, J. Kim, and S. Hwang, "Comparison of Out-of-Distribution Detection Performance of CLIP-based Fine-Tuning Methods", 2024 International Conference on Electronics, Information, and Communication (ICEIC), 2024.01. (Best Paper Award) [paper]

[IJ] J. Kim, J. Koo, and S. Hwang (2023), "A unified benchmark for the unknown detection capability of deep neural networks", Expert Systems with Applications, 229, 120461. [paper] [code]

[IJ] Y. Lee, S. Lee, and S. Hwang (2023), "Patch-Level Consistency Regularization in Self-Supervised Transfer Learning for Fine-Grained Image Recognition", Applied Sciences, 13(18), 10493. [paper]

[DC] 이슬비, 서승원, 황상흠, "시간 정보의 명시적인 반영을 통한 LTSF-Linear 모델의 예측 성능 개선", 한국정보과학회 2023 한국컴퓨터종합학술대회 논문집, 1791-1793. (학부생논문경진대회 장려상) [paper]

[DC] 서승원, 이수호, 황상흠, "Cross-Attention을 활용한 새로운 전이 학습 기법", 한국정보과학회 2023 한국컴퓨터종합학술대회 논문집, 486-488. (우수논문상) [paper]

[DC] 김범수, 김지효, 강민준, 문대정, 황상흠, "기준이 불명확한 분류 문제에 적합한 레이블링 방법: 쌍별 비교기반의 순위 레이블링", 한국정보과학회 2023 한국컴퓨터종합학술대회 논문집, 480-482. [paper]

[DC] 김정현, 김지효, 황상흠, "임의 텍스트 미세조정 학습을 통한 CLIP 모델의 학습 외 분포 데이터 탐지 성능 향상 방법", 한국정보과학회 2023 한국컴퓨터종합학술대회 논문집, 593-595.  (우수논문상) [paper]

[DJ] 서승원, 황상흠, "EDAD: 도메인 적응과 지식 증류를 통합한 효율적 도메인 적응 증류", 대한산업공학회지, 49(2), 133-141. [paper]

[IJ] J. Park, S. Shin, S. Hwang, and S. Choi, "Elucidating robust learning with uncertainty-aware corruption pattern estimation", Pattern Recognition, 138, 109387. [paper]

[arXiv] J. Lee, D. Yoon, B. Ji, K. Kim, and S. Hwang, "Rethinking Evaluation Protocols of Visual Representations Learned via Self-supervised Learning", arXiv preprint arXiv:2304.03456. [paper]

[arXiv] S. Lee, S. Seo, J. Kim, Y. Lee, and S. Hwang, "Few-shot Fine-tuning is All You Need for Source-free Domain Adaptation", arXiv preprint arXiv:2304.00792. [paper]

[IC] J. Kim, J. Kim, and S. Hwang, "Deep Active Learning with Contrastive Learning Under Realistic Data Pool Assumptions", AAAI 2023 Workshop on Practical Deep Learning in the Wild [paper]

[DC] 이수진, 황상흠, "클릭률 예측을 위한 컨텍스트 기반의 교차 어텐션 추천 시스템", 대한산업공학회 추계학술대회, 2022.11. [slide]

[DC] 김성철, 문대정, 김범수, 서승원, 김지효, 황상흠, "자기 지도 학습과 메타 정보를 활용한 피부 상태 판별 연구", 대한산업공학회 추계학술대회, 2022.11. [slide]

[DC] C. Vintimilla, S. Hwang, "Self-supervised Representation Learning for Basecalling Oxford Nanopore Sequencing Data", 대한산업공학회 추계학술대회, 2022.11. [slide]

[IC] K. You, S. Lee*, K. Jo, E. Park, T. Kooi, and H. Nam, "Intra-class Contrastive Learning Improves Computer Aided Diagnosis of Breast Cancer in Mammography", International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2022. (*Work done during an internship at Lunit) [paper]

[DC] 김지효, 황상흠, "현실적인 데이터 풀을 고려한 능동적 학습 방법 비교연구", 한국정보과학회 2022 한국컴퓨터종합학술대회 논문집, 921-923. [paper]

[DC] 이예진, 황상흠, "세밀한 객체 인식을 위한 자기 지도 학습 모델의 전이 학습", 한국정보과학회 2022 한국컴퓨터종합학술대회 논문집, 927-929. (우수논문상) [paper]

[DC] 서승원, 황상흠, "EDAD: 자연어 처리에서의 효율적 도메인 적응 증류", 한국정보과학회 2022 한국컴퓨터종합학술대회 논문집, 924-926. [paper]

[IJ] H. Shin, S. Sim, H. Kwon, S. Hwang, and Y. Lee (2021), "A New Smart Smudge Attack Using CNN", International Journal of Information Security, 21(1), 25-36. [paper]

[DC] 이상우, 이예진, 황상흠, "대조적 손실 함수를 활용한 영역 분할 모델의 도메인 강건성 개선", 대한전기학회 학술대회 논문집, 2021.10. [paper]

[arXiv] J. Kim, J. Koo, and S. Hwang, "A Unified Benchmark for the Unknown Detection Capability of Deep Neural Networks", arXiv preprint arXiv:2112.00337. [paper]

[arXiv] J. Park, S. Shin, S. Hwang, and S. Choi, "Elucidating Noisy Data via Uncertainty-Aware Robust Learning", arXiv preprint arXiv:2111.01632. [paper]

[DC] 문대정, 황재문, 김지효, 황상흠, "한글 문서 OCR에서의 상용 API 성능 비교 연구", 대한산업공학회 추계학술대회. [slide]

[IJ] S. Lee*, Y. Lee*, G. Lee, and S. Hwang (2021), "Supervised Contrastive Embedding for Medical Image Segmentation", IEEE Access, 9, 138403-138414. (*equal contribution) [paper] [code]

[IC] K. Kim, B. M. Ji, D. Yoon, and S. Hwang, "Self-Knowledge Distillation with Progressive Refinement of Targets", International Conference on Computer Vision (ICCV), 2021. (Oral presentation) [paper] [code]

[IJ] S. Lee, E. Jo, S. Hwang, G. Bok Jung, and D. Kim (2021), "Similarity-Based Deep Neural Networks", International Journal of Fuzzy Logic and Intelligent Systems, 21(3), 205-212. [paper]

[DC] 배준호, 황상흠, "의료 영상 분할에서의 도메인 일반화를 위한 형태 정보의 활용", 대한산업공학회 춘계공동학술대회. [slide]

[DC] 최찬희, 황상흠, "딥러닝의 예측 강건성 측면에서의 모델 경량화 효과 연구", 대한산업공학회 춘계공동학술대회. [slide]

[DC] 양승무, 황상흠, "특정 도메인을 위한 질의응답 시스템의 불확실성 회피 능력에 대한 고찰", 대한산업공학회 춘계공동학술대회. [slide]

[DC] 구지인, 황상흠, "Mixup을 활용한 이상 입력 탐지 모델의 불확실성 추정 능력 개선", 대한산업공학회 춘계공동학술대회. [slide]

[IJ] J. Koo and S. Hwang, "A Unified Defect Pattern Analysis of Wafer Maps Using Density-Based Clustering", IEEE Access, 9, 78873-78882. [paper]

[IJ] J. Hwang and S. Hwang (2021), "Exploiting Global Structure Information to Improve Medical Image Segmentation", Sensors, 21(9), 3249. [paper]

[DJ] Y. Lee, S. Lee, and S. Hwang (2021), "Improving Domain Generalization Performance for Medical Image Segmentation by Self-Supervised Learning", Journal of the Korean Institute of Industrial Engineers, 47(2), 180-189. [paper]

[DC] 이건규, 황상흠, "영역 분할 모델의 성능 향상을 위한 대조적 손실함수의 활용", 대한산업공학회 추계학술대회, 174-194. [slide]

[arXiv] K. Kim, B. M. Ji, D. Yoon, and S. Hwang, "Self-Knowledge Distillation: A Simple Way for Better Generalization", arXiv preprint arXiv:2006.12000. [paper]

[IC] J. Moon*, J. Kim*, Y. Shin, and S. Hwang, "Confidence-Aware Learning for Deep Neural Networks", International Conference on Machine Learning (ICML), 2020. (*equal contribution) [paper] [code]

[IJ] S. Hwang, H. G. Yeo, and J.-S. Hong (2020), "A New Splitting Criterion for Better Interpretable Trees", IEEE Access, 8, 62762-62774. [paper]

[IJ] M. Park, S. Lee, S. Hwang, and D. Kim (2020), "Additive Ensemble Neural Networks", IEEE Access, 8, 113192-113199. [paper]

[DJ] S. Hwang and D. Kim (2020), "BERT-based Classification Model for Korean Documents", Journal of Society for e-Business Studies, 25(1), 203-214. [paper]

[IJ] M. Veta, [et al. including S. Hwang] (2019), “Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge,” Medical Image Analysis, 290(1), 218-228. [paper]

[IJ] J. G. Nam, S. Park, [et al. including S. Hwang] (2019), “Development and validation of deep Learning–based automatic detection algorithm for malignant pulmonary nodules on chest radiographs,” Radiology, 290(1), 218-228. [paper]

[DC] 김수민, 황상흠, 윤동희, 김도현, "Unsupervised Feature Selection for Autoencoder", 한국경영과학회 학술대회, 1330-1356. [slide]

[DC] 문주영, 김지효, 황상흠, "심층 신경망의 과한 확신을 방지하는 새로운 정규화 방법", 대한산업공학회 추계학술대회, 2522-2540. [slide]

[DC] 황재문, 황상흠, "해부학적 구조를 반영한 흉부 X-ray 영상에서의 폐 영역 분할 모델", 대한산업공학회 추계학술대회, 2505-2521. [slide]

[IJ] S. Hwang, and D. Kim (2018), “A scalable feature based clustering algorithm for sequences with many distinct items,” International Journal of Fuzzy Logic and Intelligent Systems, 18(4), 316-325. [paper]

[IJ] S. Hwang, and M. K. Jeong (2018), “Robust relevance vector machine for classification with variational inference,” Annals of Operations Research, 263(1-2), 21-43. [paper]

[DC] 여현규, 황상흠, 홍정식, "의사결정나무 분류 모델 해석력 향상을 위한 새로운 분기 기준", 한국경영과학회 학술대회, 560-571. [slide]

[IC] S. Lee, S. Hwang, D. Kim, and E. Jo (2018), "Deep neural networks with small data", 2018 INFORMS International Conference, Jun. 2018.

[IC] S. Hwang, and S. Park, “Accurate lung segmentation via network-wise training of convolutional networks,” The 3rd International Workshop on Deep Learning in Medical Image Analysis in MICCAI 2017, Sep. 2017.

[IC] K. Paeng, S. Hwang, S. Park, and M. Kim, “A unified framework for tumor proliferation score prediction in breast histopathology,” The 3rd International Workshop on Deep Learning in Medical Image Analysis in MICCAI 2017, Sep. 2017.

[IJ] S. Hwang, J. Yoo, C. Lee, and S. H. Lee (2016), “Collaborative crystal structure prediction,” Expert Systems with Applications, 63, 222-230.

[IC] S. Hwang, and H.-E. Kim, “Self-transfer learning for weakly supervised lesion localization,” The 19th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 239-246, Oct. 2016.

[arxiv] H.-E. Kim, S. Hwang, and K. Cho (2016), "Semantic Noise Modeling for Better Representation Learning", arXiv preprint arXiv:1611.01268.

[arxiv] H.-E. Kim, and S. Hwang, (2016), "Deconvolutional feature stacking for weakly-supervised semantic segmentation", arXiv preprint arXiv:1602.04984.

[IC] S. Hwang, H.-E. Kim, J. Jeong, and H.-J. Kim, “A novel approach for tuberculosis screening based on deep convolutional neural networks,” in Proceedings of SPIE Medical Imaging, 9785, Mar. 2016.

[IC] S. Kim, J. Lee, S. Hwang, S. Cho, S. Hwang, H.-E. Kim, H. Shim, M. Yang, and S. Song, “Deep convolutional neural network-based mitosis detection in invasive carcinoma of breast by smartphone-based histologic image acquisition,” in Modern Pathology (USCAP Annual Meeting), 29, Mar. 2016.

[IJ] Y.-S. Jeong, S. Hwang, and Y.-D. Ko (2015), “Quantitative analysis for plasma etch modeling using optical emission spectroscopy: prediction of plasma etch responses,” Industrial Engineering and Management Systems, 14(4), 392-400.

[IJ] D. Kim, C. Lee, S. Hwang, and M. K. Jeong (2015), “A robust support vector regression with a linear-log concave loss function,” Journal of Operational Research Society, 67(5), 735-742.

[IJ] S. Hwang, D. Kim, M. K. Jeong, and B.-J. Yum (2015), “Robust kernel based regression with bounded influence for outliers,” Journal of Operational Research Society, 66(8), 1385-1398.

[IJ] D. Mishra, Y.-H. Cho, M.-B. Shim, S. Hwang, S. Kim, C. Y. Park, S. Y. Seo, S.-H. Yoo, S.-H. Park, and Y. E. Pak (2015), “Effect of piezoelectricity on critical thickness for misfit dislocation formation at InGaN/GaN interface,” Computational Materials Science, 97, 254-262.

[IJ] S. Hwang, M. K. Jeong, and B.-J. Yum (2014), “Robust relevance vector machine with variational inference for improving virtual metrology accuracy,” IEEE Transactions on Semiconductor Manufacturing, 27(1), 83-94.

[DC] S. Hwang, "소재개발 가속화를 위한 시뮬레이션 플랫폼의 개발: 데이터 기반 결정 구조 예측 방법", 대한산업공학회 춘계공동학술대회, 2014.

[IJ] Y.-H. Cho, J.-Y. Kim, J. Kim, M.-B. Shim, S. Hwang, S.-H. Park, Y.-S. Park, and S. Kim (2013), “Quantum efficiency affected by localized carrier distribution near the V-defect in GaN based quantum well,” Applied Physics Letters, 103, 261101.

[IJ] S.-H. Park, D. Mishra, Y. E. Pak, C. Y. Park, S.-H. Yoo, Y.-H. Cho, M.-B. Shim, S. Hwang, and S. Kim (2013), “Partial strain relaxation effects on polarization anisotropy of semipolar (1122) InGaN/GaN quantum well structures,” Applied Physics Letters, 103, 221108.

[IC] S. Hwang, N. Kim, B. J. Yum, and M. K. Jeong, "Robust Kernel Based Regression with Bounded Influence for Outliers", INFORMS Annual Meeting, 2010. (Data Mining Student Paper Award, 2nd place)

[DC] S. Bae, S. Hwang, S. H. Ha, and T. Lee, “How can online sellers use blogs as online consumer review sources?,” in Proceedings of the Conference of Korean Operations Research and Management Science, Jun. 2010.

[IC] S. Hwang and B. J. Yum, "A Scalable Clustering Algorithm for Sparse Sequence Data", INFORMS Annual Meeting, 2008.