MixCo: Mix-up Contrastive Learning for Visual Representation. - Sungnyun Kim; Gihun Lee; Sangmin Bae; Se-Young Yun (CoRR, 2020)

Self-Contrastive Learning. - Sangmin Bae; Sungnyun Kim; Jongwoo Ko; Gihun Lee; Seungjong Noh; Se-Young Yun (CoRR, 2021)

ReFine: Re-randomization before Fine-tuning for Cross-domain Few-shot Learning. - Jaehoon Oh; Sungnyun Kim; Namgyu Ho; Jin-Hwa Kim; Hwanjun Song; Se-Young Yun (CIKM, 2022)

Real-time and Explainable Detection of Epidemics with Global News Data. - Sungnyun Kim; Jaewoo Shin; Seongha Eom; Jihwan Oh; Se-Young Yun (Healthcare AI and COVID-19 Workshop, 2022)

Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty. - Jaehoon Oh; Sungnyun Kim; Namgyu Ho; Jin-Hwa Kim; Hwanjun Song; Se-Young Yun (NeurIPS, 2022)

Calibration of Few-Shot Classification Tasks: Mitigating Misconfidence From Distribution Mismatch. - Sungnyun Kim; Se-Young Yun (IEEE Access, 2022)

Understanding Cross-Domain Few-Shot Learning: An Experimental Study. - Jaehoon Oh; Sungnyun Kim; Namgyu Ho; Jin-Hwa Kim; Hwanjun Song; Se-Young Yun (CoRR, 2022)

Revisiting the Updates of a Pre-trained Model for Few-shot Learning. - Yujin Kim; Jaehoon Oh; Sungnyun Kim; Se-Young Yun (CoRR, 2022)

Self-Contrastive Learning: Single-Viewed Supervised Contrastive Framework Using Sub-network. - Sangmin Bae; Sungnyun Kim; Jongwoo Ko; Gihun Lee; Seungjong Noh; Se-Young Yun (AAAI, 2023)

Coreset Sampling from Open-Set for Fine-Grained Self-Supervised Learning. - Sungnyun Kim; Sangmin Bae; Se-Young Yun (CVPR, 2023)

Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Classification. - Sangmin Bae; June-Woo Kim; Won-Yang Cho; Hyerim Baek; Soyoun Son; Byungjo Lee; Changwan Ha; Kyongpil Tae; Sungnyun Kim; Se-Young Yun (INTERSPEECH, 2023)

Recycle-and-Distill: Universal Compression Strategy for Transformer-based Speech SSL Models with Attention Map Reusing and Masking Distillation. - Kangwook Jang; Sungnyun Kim; Se-Young Yun; Hoirin Kim (INTERSPEECH, 2023)

DiffBlender: Scalable and Composable Multimodal Text-to-Image Diffusion Models. - Sungnyun Kim; Junsoo Lee; Kibeom Hong; Daesik Kim; Namhyuk Ahn (CoRR, 2023)

DocKD: Knowledge Distillation from LLMs for Open-World Document Understanding Models. - Sungnyun Kim; Haofu Liao; Srikar Appalaraju; Peng Tang; Zhuowen Tu; Ravi Kumar Satzoda; R. Manmatha; Vijay Mahadevan; Stefano Soatto (EMNLP, 2024)

STaR: Distilling Speech Temporal Relation for Lightweight Speech Self-Supervised Learning Models. - Kangwook Jang; Sungnyun Kim; Hoirin Kim (ICASSP, 2024)

DistiLLM: Towards Streamlined Distillation for Large Language Models. - Jongwoo Ko; Sungnyun Kim; Tianyi Chen; Se-Young Yun (ICML, 2024)

Learning Video Temporal Dynamics With Cross-Modal Attention For Robust Audio-Visual Speech Recognition. - Sungnyun Kim; Kangwook Jang; Sangmin Bae; Hoirin Kim; Se-Young Yun (SLT, 2024)

Diffusion-based Episodes Augmentation for Offline Multi-Agent Reinforcement Learning. - Jihwan Oh; Sungnyun Kim; Gahee Kim; Sunghwan Kim; Se-Young Yun (CoRR, 2024)

Multi-Task Corrupted Prediction for Learning Robust Audio-Visual Speech Representation. - Sungnyun Kim; Sungwoo Cho; Sangmin Bae; Kangwook Jang; Se-Young Yun (ICLR, 2025)

FedDr+: Stabilizing Dot-regression with Global Feature Distillation for Federated Learning. - Seongyoon Kim; Minchan Jeong; Sungnyun Kim; Sungwoo Cho; Sumyeong Ahn; Se-Young Yun (Trans. Mach. Learn. Res., 2025)

MoHAVE: Mixture of Hierarchical Audio-Visual Experts for Robust Speech Recognition. - Sungnyun Kim; Kangwook Jang; Sangmin Bae; Sungwoo Cho; Se-Young Yun (CoRR, 2025)

DistiLLM-2: A Contrastive Approach Boosts the Distillation of LLMs. - Jongwoo Ko; Tianyi Chen; Sungnyun Kim; Tianyu Ding; Luming Liang; Ilya Zharkov; Se-Young Yun (CoRR, 2025)

MAVFlow: Preserving Paralinguistic Elements with Conditional Flow Matching for Zero-Shot AV2AV Multilingual Translation. - Sungwoo Cho; Jeongsoo Choi; Sungnyun Kim; Se-Young Yun (CoRR, 2025)

Flex-Judge: Think Once, Judge Anywhere. - Jongwoo Ko; Sungnyun Kim; Sungwoo Cho; Se-Young Yun (CoRR, 2025)

Mixture-of-Recursions: Learning Dynamic Recursive Depths for Adaptive Token-Level Computation. - Sangmin Bae; Yujin Kim; Reza Bayat; Sungnyun Kim; Jiyoun Ha; Tal Schuster; Adam Fisch; Hrayr Harutyunyan; Ziwei Ji; Aaron Courville; Se-Young Yun (CoRR, 2025)