In this talk we will overview the interplay between Deep Learning (DL) and Compressive Sampling (CS). We will provide an overview of prominent DL-based CS reconstruction algorithms, with a specific emphasis on practical implementation considerations. We will also discuss joint sensing-and-reconstruction DL optimization approaches for various sensing matrix types. The effectiveness of this design will be demonstrated through face compressive imaging using only a few samples. Additionally, we will explore how CS can safeguard DL from adversarial attacks.
Adrian Stern is a Professor at the School of ECE at Ben-Gurion University in Israel, where he serves as department head School Deputy Head for Research. Previously, he served as the head of the Electro-Optical Engineering Department. He has held visiting scholar and professor positions at MIT and UConn.
Dr. Stern has published over 200 technical articles in leading peer-reviewed journals and conference proceedings. His current research interests include Scientific Deep Learning, Compressive Imaging and optical Sensing, 3D imaging, hyperspectral imaging, remote Sensing, and deep learning security.
Dr. Stern is an elected Fellow of Optica (formerly Optical Society of America) and of SPIE. He chaired and co-chaired several SPIE and OSA conferences. He has been an editor for several journals and is the editor of the book Optical Compressive Imaging.