אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Anomaly detection aims to discover data which differ from the norm in a semantically meaningful manner. The task is difficult as anomalies are rare and unexpected. Moreover, a sample can be an important anomaly to one person and an uninteresting statistical outlier to another. In this talk, I will first present how deep representations brought substantial gains for image anomaly detection and segmentation. Next, we will discuss the types of representations that are beneficial for anomaly detection, and how a given representation can be improved. Finally, I will present two remaining challenges and initial directions toward addressing them: (i) Strong nuisance variation, unrelated to the attributes we wish to inspect, may bias our representation (ii) Unexpected fine-grained combinations of normal parts (“logical anomalies”) may appear normal with coarse-grained representations. Bio: Niv is a Ph.D. student at the Hebrew University of Jerusalem, advised by Dr. Yedid Hoshen. He received his BSc. in mathematics with physics, and M.Sc. in physics, both from the Technion. He's interested in computer vision and representation learning with a focus on anomaly detection and scientific data.