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)

Use and Perceptions of Multi-Monitor Workstations: A Natural Experiment. - Guy Amir; Ayala Prusak; Tal Reiss; Nir Zabari; Dror G. Feitelson (SER&IP@ICSE, 2021)

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

Efficient Discovery and Effective Evaluation of Visual Perceptual Similarity: A Benchmark and Beyond. - Oren Barkan; Tal Reiss; Jonathan Weill; Ori Katz; Roy Hirsch; Itzik Malkiel; Noam Koenigstein (ICCV, 2023)

No Free Lunch: The Hazards of Over-Expressive Representations in Anomaly Detection. - Tal Reiss; Niv Cohen; 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)

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

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)