| Patch svdd: Patch-level svdd for anomaly detection and segmentation J Yi, S Yoon Proceedings of the Asian conference on computer vision, 2020 | 400 | 2020 |
| Deep learning for anomaly detection in time-series data: Review, analysis, and guidelines K Choi, J Yi, C Park, S Yoon IEEE access 9, 120043-120065, 2021 | 265 | 2021 |
| Bbam: Bounding box attribution map for weakly supervised semantic and instance segmentation J Lee, J Yi, C Shin, S Yoon Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 156 | 2021 |
| Interpretation of NLP models through input marginalization S Kim, J Yi, E Kim, S Yoon arXiv preprint arXiv:2010.13984, 2020 | 53 | 2020 |
| Removing undesirable feature contributions using out-of-distribution data S Lee, C Park, H Lee, J Yi, J Lee, S Yoon arXiv preprint arXiv:2101.06639, 2021 | 23 | 2021 |
| Energy-based sequence gans for recommendation and their connection to imitation learning J Yoo, H Ha, J Yi, J Ryu, C Kim, JW Ha, YH Kim, S Yoon arXiv preprint arXiv:1706.09200, 2017 | 12 | 2017 |
| iCaps: An interpretable classifier via disentangled capsule networks D Jung, J Lee, J Yi, S Yoon European Conference on Computer Vision, 314-330, 2020 | 10 | 2020 |
| Information-theoretic visual explanation for black-box classifiers J Yi, E Kim, S Kim, S Yoon arXiv preprint arXiv:2009.11150, 2020 | 5 | 2020 |
| Appendix for BBAM: Bounding Box Attribution Map for Weakly Supervised Semantic and Instance Segmentation J Lee, J Yi, C Shin, S Yoon | | |
| Supplementary materials for “Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentation” J Yi, S Yoon | | |
| Supplementary material for iCaps: An Interpretable Classifier via Disentangled Capsule Networks D Jung, J Lee, J Yi, S Yoon | | |
