| Sub-image anomaly detection with deep pyramid correspondences N Cohen, Y Hoshen arXiv preprint arXiv:2005.02357, 2020 | 589 | 2020 |
| Classification-Based Anomaly Detection for General Data L Bergman, Y Hoshen International Conference on Learning Representations (ICLR 2020), 2020 | 464 | 2020 |
| 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 | 324 | 2021 |
| VAIN: Attentional Multi-agent Predictive Modeling Y Hoshen Advances in Neural Information Processing Systems (NIPS'17), 2017 | 312 | 2017 |
| Speech Acoustic Modeling from Raw Multichannel Waveforms Y Hoshen, R Weiss, KW Wilson IEEE International Conference on Acoustics, Speech and Signal Processing, 2015 | 293 | 2015 |
| Processing multi-channel audio waveforms TN Sainath, RJ Weiss, KW Wilson, AW Senior, A Narayanan, Y Hoshen, ... US Patent 9,697,826, 2017 | 258 | 2017 |
| Deep nearest neighbor anomaly detection L Bergman, N Cohen, Y Hoshen arXiv preprint arXiv:2002.10445, 2020 | 195 | 2020 |
| 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 | 178 | 2023 |
| Mean-shifted contrastive loss for anomaly detection T Reiss, Y Hoshen AAAI'23, 2023 | 138 | 2023 |
| Non-adversarial unsupervised word translation Y Hoshen, L Wolf EMNLP'18, 2018 | 132* | 2018 |
| Demystifying Inter-Class Disentanglement A Gabbay, Y Hoshen International Conference on Learning Representations (ICLR 2020), 2020 | 73 | 2020 |
| 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 | 71* | 2023 |
| Non-Adversarial Image Synthesis with Generative Latent Nearest Neighbors Y Hoshen, K Li, J Malik Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019 | 69 | 2019 |
| An Egocentric Look at Video Photographer Identity Y Hoshen, S Peleg IEEE Conference on Computer Vision and Pattern Recognition (CVPR'16), 2016 | 59* | 2016 |
| Attribute-based representations for accurate and interpretable video anomaly detection T Reiss, Y Hoshen TMLR, 2025 | 45 | 2025 |
| The inductive bias of in-context learning: Rethinking pretraining example design Y Levine, N Wies, D Jannai, D Navon, Y Hoshen, A Shashua ICLR'22, 2022 | 39 | 2022 |
| Power to peep-all: Inference attacks by malicious batteries on mobile devices P Lifshits, R Forte, Y Hoshen, M Halpern, M Philipose, M Tiwari, ... Proceedings on Privacy Enhancing Technologies, 2018 | 39 | 2018 |
| An image is worth more than a thousand words: Towards disentanglement in the wild A Gabbay, N Cohen, Y Hoshen Advances in Neural Information Processing Systems, 2021, 2021 | 38 | 2021 |
| 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 | 37* | 2021 |
| Sub-image anomaly detection with deep pyramid correspondences. arXiv 2020 N Cohen, Y Hoshen arXiv preprint arXiv:2005.02357, 2005 | 37 | 2005 |
