| Dreamix: Video Diffusion Models are General Video Editors E Molad, E Horwitz, D Valevski, AR Acha, Y Matias, Y Pritch, Y Leviathan, ... arXiv preprint arXiv:2302.01329, 2023 | 139 | 2023 |
| Back to the feature: classical 3d features are (almost) all you need for 3d anomaly detection E Horwitz, Y Hoshen Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 38* | 2023 |
| Image shape manipulation from a single augmented training sample Y Vinker, E Horwitz, N Zabari, Y Hoshen Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 34* | 2021 |
| 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 |
| Conffusion: Confidence Intervals for Diffusion Models E Horwitz, Y Hoshen arXiv preprint arXiv:2211.09795, 2022 | 19 | 2022 |
| Recovering the Pre-Fine-Tuning Weights of Generative Models E Horwitz, J Kahana, Y Hoshen arXiv preprint arXiv:2402.10208, 2024 | 2 | 2024 |
| Dataset Size Recovery from LoRA Weights M Salama, J Kahana, E Horwitz, Y Hoshen arXiv preprint arXiv:2406.19395, 2024 | | 2024 |
| Real-Time Deepfake Detection in the Real-World B Cavia, E Horwitz, T Reiss, Y Hoshen arXiv preprint arXiv:2406.09398, 2024 | | 2024 |
| On the Origin of Llamas: Model Tree Heritage Recovery E Horwitz, A Shul, Y Hoshen arXiv preprint arXiv:2405.18432, 2024 | | 2024 |
| Distilling Datasets Into Less Than One Image A Shul, E Horwitz, Y Hoshen arXiv preprint arXiv:2403.12040, 2024 | | 2024 |
Yedid HoshenThe Hebrew University of Jerusalem在 mail.huji.ac.il 的电子邮件经过验证
