| PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation T Reiss, N Cohen, L Bergman, Y Hoshen Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 250 | 2021 |
| Mean-shifted contrastive loss for anomaly detection T Reiss, Y Hoshen Proceedings of the AAAI Conference on Artificial Intelligence 37 (2), 2155-2162, 2023 | 96 | 2023 |
| Attribute-based representations for accurate and interpretable video anomaly detection T Reiss, Y Hoshen arXiv preprint arXiv:2212.00789, 2022 | 25 | 2022 |
| Anomaly detection requires better representations T Reiss, N Cohen, E Horwitz, R Abutbul, Y Hoshen European Conference on Computer Vision, 56-68, 2022 | 21 | 2022 |
| Detecting deepfakes without seeing any T Reiss, B Cavia, Y Hoshen arXiv preprint arXiv:2311.01458, 2023 | 3 | 2023 |
| No free lunch: The hazards of over-expressive representations in anomaly detection T Reiss, N Cohen, Y Hoshen arXiv preprint arXiv:2306.07284, 2023 | 3 | 2023 |
| Efficient Discovery and Effective Evaluation of Visual Perceptual Similarity: A Benchmark and Beyond O Barkan, T Reiss, J Weill, O Katz, R Hirsch, I Malkiel, N Koenigstein Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 2 | 2023 |
| Use and Perceptions of Multi-Monitor Workstations: A Natural Experiment G Amir, A Prusak, T Reiss, N Zabari, DG Feitelson 2021 IEEE/ACM 8th International Workshop on Software Engineering Research …, 2021 | 2 | 2021 |
| From Zero to Hero: Cold-Start Anomaly Detection T Reiss, G Kour, N Zwerdling, A Anaby-Tavor, Y Hoshen arXiv preprint arXiv:2405.20341, 2024 | | 2024 |
| Visual search and discovery via generative model inversion O Barkan, N Zabari, T Reiss, N Koenigstein, N Nice US Patent App. 17/746,869, 2023 | | 2023 |
| Deep learning-based anomaly detection in images Y Hoshen, L Bergman, N Cohen, T Reiss US Patent App. 17/913,905, 2023 | | 2023 |
