| 2026 | SIGGRAPH | Progressive Photorealistic Simplification. | Adi Rosenthal, Dana Berman, Yedid Hoshen, Ariel Shamir |
| 2025 | CVPR | Learning on Model Weights using Tree Experts. | Eliahu Horwitz, Bar Cavia, Jonathan Kahana, Yedid Hoshen |
| 2025 | ICCV | ObjectMate: A Recurrence Prior for Object Insertion and Subject-Driven Generation. | Daniel Winter, Asaf Shul, Matan Cohen, Dana Berman, Yael Pritch, Alex Rav-Acha, Yedid Hoshen |
| 2025 | ICLR | Unsupervised Model Tree Heritage Recovery. | Eliahu Horwitz, Asaf Shul, Yedid Hoshen |
| 2025 | ICLR | Deep Linear Probe Generators for Weight Space Learning. | Jonathan Kahana, Eliahu Horwitz, Imri Shuval, Yedid Hoshen |
| 2025 | SIGGRAPH | LightLab: Controlling Light Sources in Images with Diffusion Models. | Nadav Magar, Amir Hertz, Eric Tabellion, Yael Pritch, Alex Rav-Acha, Ariel Shamir, Yedid Hoshen |
| 2024 | ACL | From Zero to Hero: Cold-Start Anomaly Detection. | Tal Reiss, George Kour, Naama Zwerdling, Ateret Anaby-Tavor, Yedid Hoshen |
| 2024 | ECCV | ObjectDrop: Bootstrapping Counterfactuals for Photorealistic Object Removal and Insertion. | Daniel Winter, Matan Cohen, Shlomi Fruchter, Yael Pritch, Alex Rav-Acha, Yedid Hoshen |
| 2024 | ICML | Recovering the Pre-Fine-Tuning Weights of Generative Models. | Eliahu Horwitz, Jonathan Kahana, Yedid Hoshen |
| 2023 | AAAI | Mean-Shifted Contrastive Loss for Anomaly Detection. | Tal Reiss, Yedid Hoshen |
| 2023 | CVPR | Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection. | Eliahu Horwitz, Yedid Hoshen |
| 2023 | ICASSP | Unsupervised Word Segmentation Using Temporal Gradient Pseudo-Labels. | Tzeviya Sylvia Fuchs, Yedid Hoshen |
| 2023 | ICLR | Red PANDA: Disambiguating Image Anomaly Detection by Removing Nuisance Factors. | Niv Cohen, Jonathan Kahana, Yedid Hoshen |
| 2022 | ECCV | Out-of-Distribution Detection Without Class Labels. | Niv Cohen, Ron Abutbul, Yedid Hoshen |
| 2022 | ECCV | A Contrastive Objective for Learning Disentangled Representations. | Jonathan Kahana, Yedid Hoshen |
| 2022 | ECCV | Anomaly Detection Requires Better Representations. | Tal Reiss, Niv Cohen, Eliahu Horwitz, Ron Abutbul, Yedid Hoshen |
| 2022 | ECCV | Open-Vocabulary Semantic Segmentation Using Test-Time Distillation. | Nir Zabari, Yedid Hoshen |
| 2022 | ICLR | The Inductive Bias of In-Context Learning: Rethinking Pretraining Example Design. | Yoav Levine, Noam Wies, Daniel Jannai, Dan Navon, Yedid Hoshen, Amnon Shashua |
| 2022 | Interspeech | Unsupervised Word Segmentation using K Nearest Neighbors. | Tzeviya Fuchs, Yedid Hoshen, Yossi Keshet |
| 2022 | RecSys | You Say Factorization Machine, I Say Neural Network - It's All in the Activation. | Chen Almagor, Yedid Hoshen |
| 2021 | CVPR | PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation. | Tal Reiss, Niv Cohen, Liron Bergman, Yedid Hoshen |
| 2021 | ICASSP | Crypto-Oriented Neural Architecture Design. | Avital Shafran, Gil Segev, Shmuel Peleg, Yedid Hoshen |
| 2021 | ICCV | Scaling-up Disentanglement for Image Translation. | Aviv Gabbay, Yedid Hoshen |
| 2021 | ICCV | Membership Inference Attacks are Easier on Difficult Problems. | Avital Shafran, Shmuel Peleg, Yedid Hoshen |
| 2021 | ICCV | Image Shape Manipulation from a Single Augmented Training Sample. | Yael Vinker, Eliahu Horwitz, Nir Zabari, Yedid Hoshen |
| 2020 | ICLR | Classification-Based Anomaly Detection for General Data. | Liron Bergman, Yedid Hoshen |
| 2020 | ICLR | Demystifying Inter-Class Disentanglement. | Aviv Gabbay, Yedid Hoshen |
| 2019 | CVPR | Non-Adversarial Image Synthesis With Generative Latent Nearest Neighbors. | Yedid Hoshen, Ke Li, Jitendra Malik |
| 2019 | ICASSP | Towards Unsupervised Single-channel Blind Source Separation Using Adversarial Pair Unmix-and-remix. | Yedid Hoshen |
| 2019 | ICML | Neural Separation of Observed and Unobserved Distributions. | Tavi Halperin, Ariel Ephrat, Yedid Hoshen |
| 2018 | CVPR | Unsupervised Correlation Analysis. | Yedid Hoshen, Lior Wolf |
| 2018 | ECCV | NAM: Non-Adversarial Unsupervised Domain Mapping. | Yedid Hoshen, Lior Wolf |
| 2018 | EMNLP | Non-Adversarial Unsupervised Word Translation. | Yedid Hoshen, Lior Wolf |
| 2018 | ICLR | Identifying Analogies Across Domains. | Yedid Hoshen, Lior Wolf |
| 2018 | ICLR | NAM - Unsupervised Cross-Domain Image Mapping without Cycles or GANs. | Yedid Hoshen, Lior Wolf |
| 2016 | AAAI | Visual Learning of Arithmetic Operation. | Yedid Hoshen, Shmuel Peleg |
| 2016 | CVPR | An Egocentric Look at Video Photographer Identity. | Yedid Hoshen, Shmuel Peleg |
| 2015 | ICASSP | Speech acoustic modeling from raw multichannel waveforms. | Yedid Hoshen, Ron J. Weiss, Kevin W. Wilson |
| 2015 | ICIP | Live video synopsis for multiple cameras. | Yedid Hoshen, Shmuel Peleg |
| 2015 | WACV | The Information in Temporal Histograms. | Yedid Hoshen, Shmuel Peleg |
| 2014 | CVPR | Wisdom of the Crowd in Egocentric Video Curation. | Yedid Hoshen, Gil Ben-Artzi, Shmuel Peleg |
| 2013 | AVSS | Efficient representation of distributions for background subtraction. | Yedid Hoshen, Chetan Arora, Yair Poleg, Shmuel Peleg |