| Confidence score for source-free unsupervised domain adaptation J Lee, D Jung, J Yim, S Yoon International conference on machine learning, 12365-12377, 2022 | 99 | 2022 |
| Joint contrastive learning for unsupervised domain adaptation C Park, J Lee, J Yoo, M Hur, S Yoon arXiv preprint arXiv:2006.10297, 2020 | 36 | 2020 |
| Removing undesirable feature contributions using out-of-distribution data S Lee, C Park, H Lee, J Yi, J Lee, S Yoon International Conference on Learning Representations (ICLR), 2021 | 26 | 2021 |
| Entropy is not Enough for Test-Time Adaptation: From the Perspective of Disentangled Factors J Lee*, D Jung*, S Lee, J Park, J Shin, U Hwang, S Yoon International Conference on Learning Representations (ICLR) (Spotlight), 2024 | 24 | 2024 |
| SF (DA) : Source-free Domain Adaptation Through the Lens of Data Augmentation U Hwang, J Lee, J Shin, S Yoon International Conference on Learning Representations (ICLR), 2024 | 14 | 2024 |
| iCaps: An interpretable classifier via disentangled capsule networks D Jung, J Lee, J Yi, S Yoon European Conference on Computer Vision, 314-330, 2020 | 13 | 2020 |
| Efficient diffusion-driven corruption editor for test-time adaptation Y Oh, J Lee, J Choi, D Jung, U Hwang, S Yoon European Conference on Computer Vision, 184-201, 2024 | 6 | 2024 |
| Gradient Alignment with Prototype Feature for Fully Test-time Adaptation J Shin, J Lee, S Lee, M Park, D Lee, U Hwang, S Yoon arXiv preprint arXiv:2402.09004, 2024 | 1 | 2024 |
| Reduce, Reuse, and Recycle: Navigating Test-Time Adaptation with OOD-Contaminated Streams J Mok, J Lee, S Lee, S Yoon | | |
