Efficient representation of distributions for background subtraction. - Yedid Hoshen; Chetan Arora 001; Yair Poleg; Shmuel Peleg (AVSS, 2013)

Wisdom of the Crowd in Egocentric Video Curation. - Yedid Hoshen; Gil Ben-Artzi; Shmuel Peleg (CVPR Workshops, 2014)

Egocentric Video Biometrics. - Yedid Hoshen; Shmuel Peleg (CoRR, 2014)

Speech acoustic modeling from raw multichannel waveforms. - Yedid Hoshen; Ron J. Weiss; Kevin W. Wilson (ICASSP, 2015)

Live video synopsis for multiple cameras. - Yedid Hoshen; Shmuel Peleg (ICIP, 2015)

The Information in Temporal Histograms. - Yedid Hoshen; Shmuel Peleg (WACV, 2015)

Visual Learning of Arithmetic Operations. - Yedid Hoshen; Shmuel Peleg (CoRR, 2015)

Human-Centric Video Summarization (שער נוסף בעברית: תקצור וידאו מנקודת מבט אנושית.). - Yedid Hoshen (2015)

Visual Learning of Arithmetic Operation. - Yedid Hoshen; Shmuel Peleg (AAAI, 2016)

An Egocentric Look at Video Photographer Identity. - Yedid Hoshen; Shmuel Peleg (CVPR, 2016)

VAIN: Attentional Multi-agent Predictive Modeling. - Yedid Hoshen (NIPS, 2017)

Unsupervised Correlation Analysis. - Yedid Hoshen; Lior Wolf (CVPR, 2018)

NAM: Non-Adversarial Unsupervised Domain Mapping. - Yedid Hoshen; Lior Wolf (ECCV (14), 2018)

Non-Adversarial Unsupervised Word Translation. - Yedid Hoshen; Lior Wolf (EMNLP, 2018)

Identifying Analogies Across Domains. - Yedid Hoshen; Lior Wolf (ICLR (Poster), 2018)

NAM - Unsupervised Cross-Domain Image Mapping without Cycles or GANs. - Yedid Hoshen; Lior Wolf (ICLR (Workshop), 2018)

Non-Adversarial Mapping with VAEs. - Yedid Hoshen (NeurIPS, 2018)

An Iterative Closest Point Method for Unsupervised Word Translation. - Yedid Hoshen; Lior Wolf (CoRR, 2018)

Non-Adversarial Image Synthesis with Generative Latent Nearest Neighbors. - Yedid Hoshen; Jitendra Malik (CoRR, 2018)

Power to peep-all: Inference Attacks by Malicious Batteries on Mobile Devices. - Pavel Lifshits; Roni Forte; Yedid Hoshen; Matt Halpern; Manuel Philipose; Mohit Tiwari; Mark Silberstein (Proc. Priv. Enhancing Technol., 2018)

Non-Adversarial Image Synthesis With Generative Latent Nearest Neighbors. - Yedid Hoshen; Ke Li 011; Jitendra Malik (CVPR, 2019)

Towards Unsupervised Single-channel Blind Source Separation Using Adversarial Pair Unmix-and-remix. - Yedid Hoshen (ICASSP, 2019)

Neural Separation of Observed and Unobserved Distributions. - Tavi Halperin; Ariel Ephrat; Yedid Hoshen (ICML, 2019)

Latent Optimization for Non-adversarial Representation Disentanglement. - Aviv Gabbay; Yedid Hoshen (CoRR, 2019)

Style Generator Inversion for Image Enhancement and Animation. - Aviv Gabbay; Yedid Hoshen (CoRR, 2019)

Classification-Based Anomaly Detection for General Data. - Liron Bergman; Yedid Hoshen (ICLR, 2020)

Demystifying Inter-Class Disentanglement. - Aviv Gabbay; Yedid Hoshen (ICLR, 2020)

Deep Nearest Neighbor Anomaly Detection. - Liron Bergman; Niv Cohen; Yedid Hoshen (CoRR, 2020)

Training End-to-end Single Image Generators without GANs. - Yael Vinker; Nir Zabari; Yedid Hoshen (CoRR, 2020)

Sub-Image Anomaly Detection with Deep Pyramid Correspondences. - Niv Cohen; Yedid Hoshen (CoRR, 2020)

Deep Single Image Manipulation. - Yael Vinker; Eliahu Horwitz; Nir Zabari; Yedid Hoshen (CoRR, 2020)

Improving Style-Content Disentanglement in Image-to-Image Translation. - Aviv Gabbay; Yedid Hoshen (CoRR, 2020)

PANDA - Adapting Pretrained Features for Anomaly Detection. - Tal Reiss; Niv Cohen; Liron Bergman; Yedid Hoshen (CoRR, 2020)

PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation. - Tal Reiss; Niv Cohen; Liron Bergman; Yedid Hoshen (CVPR, 2021)

Crypto-Oriented Neural Architecture Design. - Avital Shafran; Gil Segev 001; Shmuel Peleg; Yedid Hoshen (ICASSP, 2021)

Scaling-up Disentanglement for Image Translation. - Aviv Gabbay; Yedid Hoshen (ICCV, 2021)

Membership Inference Attacks are Easier on Difficult Problems. - Avital Shafran; Shmuel Peleg; Yedid Hoshen (ICCV, 2021)

Image Shape Manipulation from a Single Augmented Training Sample. - Yael Vinker; Eliahu Horwitz; Nir Zabari; Yedid Hoshen (ICCV, 2021)

An Image is Worth More Than a Thousand Words: Towards Disentanglement in The Wild. - Aviv Gabbay; Niv Cohen; Yedid Hoshen (NeurIPS, 2021)

Reconstruction-Based Membership Inference Attacks are Easier on Difficult Problems. - Avital Shafran; Shmuel Peleg; Yedid Hoshen (CoRR, 2021)

The Single-Noun Prior for Image Clustering. - Niv Cohen; Yedid Hoshen (CoRR, 2021)

Semantic Segmentation In-the-Wild Without Seeing Any Segmentation Examples. - Nir Zabari; Yedid Hoshen (CoRR, 2021)

Out-of-Distribution Detection Without Class Labels. - Niv Cohen; Ron Abutbul; Yedid Hoshen (ECCV Workshops (2), 2022)

A Contrastive Objective for Learning Disentangled Representations. - Jonathan Kahana; Yedid Hoshen (ECCV (26), 2022)

Anomaly Detection Requires Better Representations. - Tal Reiss; Niv Cohen; Eliahu Horwitz; Ron Abutbul; Yedid Hoshen (ECCV Workshops (4), 2022)

Open-Vocabulary Semantic Segmentation Using Test-Time Distillation. - Nir Zabari; Yedid Hoshen (ECCV Workshops (2), 2022)

The Inductive Bias of In-Context Learning: Rethinking Pretraining Example Design. - Yoav Levine; Noam Wies; Daniel Jannai; Dan Navon; Yedid Hoshen; Amnon Shashua (ICLR, 2022)

Unsupervised Word Segmentation using K Nearest Neighbors. - Tzeviya Fuchs; Yedid Hoshen; Yossi Keshet (INTERSPEECH, 2022)

You Say Factorization Machine, I Say Neural Network - It's All in the Activation. - Chen Almagor; Yedid Hoshen (RecSys, 2022)

Time Series Anomaly Detection by Cumulative Radon Features. - Yedid Hoshen (CoRR, 2022)

An Empirical Investigation of 3D Anomaly Detection and Segmentation. - Eliahu Horwitz; Yedid Hoshen (CoRR, 2022)

Red PANDA: Disambiguating Anomaly Detection by Removing Nuisance Factors. - Niv Cohen; Jonathan Kahana; Yedid Hoshen (CoRR, 2022)

Conffusion: Confidence Intervals for Diffusion Models. - Eliahu Horwitz; Yedid Hoshen (CoRR, 2022)

Improving Zero-Shot Models with Label Distribution Priors. - Jonathan Kahana; Niv Cohen; Yedid Hoshen (CoRR, 2022)

Attribute-based Representations for Accurate and Interpretable Video Anomaly Detection. - Tal Reiss; Yedid Hoshen (CoRR, 2022)

Mean-Shifted Contrastive Loss for Anomaly Detection. - Tal Reiss; Yedid Hoshen (AAAI, 2023)

Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection. - Eliahu Horwitz; Yedid Hoshen (CVPR Workshops, 2023)

Unsupervised Word Segmentation Using Temporal Gradient Pseudo-Labels. - Tzeviya Sylvia Fuchs; Yedid Hoshen (ICASSP, 2023)

Red PANDA: Disambiguating Image Anomaly Detection by Removing Nuisance Factors. - Niv Cohen; Jonathan Kahana; Yedid Hoshen (ICLR, 2023)

Dreamix: Video Diffusion Models are General Video Editors. - Eyal Molad; Eliahu Horwitz; Dani Valevski; Alex Rav-Acha; Yossi Matias; Yael Pritch; Yaniv Leviathan; Yedid Hoshen (CoRR, 2023)

Set Features for Fine-grained Anomaly Detection. - Niv Cohen; Issar Tzachor; Yedid Hoshen (CoRR, 2023)

No Free Lunch: The Hazards of Over-Expressive Representations in Anomaly Detection. - Tal Reiss; Niv Cohen; Yedid Hoshen (CoRR, 2023)

Representation Learning in Anomaly Detection: Successes, Limits and a Grand Challenge. - Yedid Hoshen (CoRR, 2023)

Detecting Deepfakes Without Seeing Any. - Tal Reiss; Bar Cavia; Yedid Hoshen (CoRR, 2023)

From Zero to Hero: Cold-Start Anomaly Detection. - Tal Reiss; George Kour; Naama Zwerdling; Ateret Anaby-Tavor; Yedid Hoshen (ACL (Findings), 2024)

ObjectDrop: Bootstrapping Counterfactuals for Photorealistic Object Removal and Insertion. - Daniel Winter; Matan Cohen; Shlomi Fruchter; Yael Pritch; Alex Rav-Acha; Yedid Hoshen (ECCV (77), 2024)

Recovering the Pre-Fine-Tuning Weights of Generative Models. - Eliahu Horwitz; Jonathan Kahana; Yedid Hoshen (ICML, 2024)

Classifying Nodes in Graphs without GNNs. - Daniel Winter; Niv Cohen; Yedid Hoshen (CoRR, 2024)

On the Origin of Llamas: Model Tree Heritage Recovery. - Eliahu Horwitz; Asaf Shul; Yedid Hoshen (CoRR, 2024)

Real-Time Deepfake Detection in the Real-World. - Bar Cavia; Eliahu Horwitz; Tal Reiss; Yedid Hoshen (CoRR, 2024)

Dataset Size Recovery from LoRA Weights. - Mohammad Salama; Jonathan Kahana; Eliahu Horwitz; Yedid Hoshen (CoRR, 2024)

Representing Model Weights with Language using Tree Experts. - Eliahu Horwitz; Bar Cavia; Jonathan Kahana; Yedid Hoshen (CoRR, 2024)

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 (CoRR, 2024)

Set Features for Anomaly Detection. - Niv Cohen; Issar Tzachor; Yedid Hoshen (Trans. Mach. Learn. Res., 2024)

Learning on Model Weights using Tree Experts. - Eliahu Horwitz; Bar Cavia; Jonathan Kahana; Yedid Hoshen (CVPR, 2025)

Unsupervised Model Tree Heritage Recovery. - Eliahu Horwitz; Asaf Shul; Yedid Hoshen (ICLR, 2025)

Deep Linear Probe Generators for Weight Space Learning. - Jonathan Kahana; Eliahu Horwitz; Imri Shuval; Yedid Hoshen (ICLR, 2025)

Enabling Users to Falsify Deepfake Attacks. - Tal Reiss; Bar Cavia; Yedid Hoshen (Trans. Mach. Learn. Res., 2025)

An Attribute-based Method for Video Anomaly Detection. - Tal Reiss; Yedid Hoshen (Trans. Mach. Learn. Res., 2025)

Distilling Datasets Into Less Than One Image. - Asaf Shul; Eliahu Horwitz; Yedid Hoshen (Trans. Mach. Learn. Res., 2025)

Can this Model Also Recognize Dogs? Zero-Shot Model Search from Weights. - Jonathan Kahana; Or Nathan; Eliahu Horwitz; Yedid Hoshen (CoRR, 2025)

Charting and Navigating Hugging Face's Model Atlas. - Eliahu Horwitz; Nitzan Kurer; Jonathan Kahana; Liel Amar; Yedid Hoshen (CoRR, 2025)

LightLab: Controlling Light Sources in Images with Diffusion Models. - Nadav Magar; Amir Hertz; Eric Tabellion; Yael Pritch; Alex Rav-Acha; Ariel Shamir; Yedid Hoshen (CoRR, 2025)