Hyounguk Shon
Affiliation: KAIST
Daejeon, South Korea
Currently a PhD student at KAIST SIIT Lab. My supervisor is Prof. Junmo Kim and I am also advised by Prof. Yunho Jeon. I am interested in machine learning, deep learning, and computer vision. My recent works involve continual learning and unlearning, noisy label learning, multi-modal contrastive learning, and scalable hyperparameter search. Previously, I was a research intern at LG AI Research mentored by Dr. Janghyeon Lee.
news
Jul 22, 2023 | In our new paper for ICCV 2023, we explore machine unlearning in the context of transfer learning. We introduce a transfer learning strategy titled Disposible Transfer Learning (DTL). DTL filters out extra information during fine-tuning, which is useful for limiting the risk of exposing the pre-trained model when publishing the expert model. Our goal is to help continue open-source AI in the era of foundation models via striking a balance between transparency and security of ownership. |
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publications
2024
2023
- ICCVDisposable Transfer Learning for Selective Source Task UnlearningIn Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), Oct 2023
- ICRALightweight Monocular Depth Estimation via Token-Sharing TransformerIn IEEE International Conference on Robotics and Automation, Oct 2023
2022
- NeurIPSUniCLIP: Unified Framework for Contrastive Language-Image Pre-trainingIn Advances in Neural Information Processing Systems, Oct 2022
- ECCVOn the Angular Update and Hyperparameter Tuning of a Scale-Invariant NetworkIn European Conference on Computer Vision, Oct 2022
2021
- CVPRJoint Negative and Positive Learning for Noisy LabelsIn Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2021