| 2025 | ICCV | Controllable Feature Whitening for Hyperparameter-Free Bias Mitigation. | Yooshin Cho, Hanbyel Cho, Janghyeon Lee, Hyeong Gwon Hong, Jaesung Ahn, Junmo Kim |
| 2024 | AAAI | Foreseeing Reconstruction Quality of Gradient Inversion: An Optimization Perspective. | Hyeong Gwon Hong, Yooshin Cho, Hanbyel Cho, Jaesung Ahn, Junmo Kim |
| 2023 | ICCV | Disposable Transfer Learning for Selective Source Task Unlearning. | Seunghee Koh, Hyounguk Shon, Janghyeon Lee, Hyeong Gwon Hong, Junmo Kim |
| 2023 | ICIP | Data Poisoning Attack Aiming the Vulnerability of Continual Learning. | Gyojin Han, Jaehyun Choi, Hyeong Gwon Hong, Junmo Kim |
| 2022 | ACSAC | Closing the Loophole: Rethinking Reconstruction Attacks in Federated Learning from a Privacy Standpoint. | Seung Ho Na, Hyeong Gwon Hong, Junmo Kim, Seungwon Shin |
| 2022 | ICIP | Rethinking Efficacy of Softmax for Lightweight Non-local Neural Networks. | Yooshin Cho, Youngsoo Kim, Hanbyel Cho, Jaesung Ahn, Hyeong Gwon Hong, Junmo Kim |
| 2021 | WACV | De-biasing Neural Networks with Estimated Offset for Class Imbalanced Learning. | Byungju Kim, Hyeong Gwon Hong, Junmo Kim |
| 2020 | AAAI | Residual Continual Learning. | Janghyeon Lee, Donggyu Joo, Hyeong Gwon Hong, Junmo Kim |
| 2020 | CVPR | Continual Learning With Extended Kronecker-Factored Approximate Curvature. | Janghyeon Lee, Hyeong Gwon Hong, Donggyu Joo, Junmo Kim |