Speaker:
Dr. Jianfei Li,
Ludwig-Maximilians-Universität München
Hosts: Jean-Luc Starck and Panagiotis Tsakalides
Seminar Title: U-Net: Mathematical Foundations of Sparse Feature Learning and Quantitative Analysis of Hallucination in Image Processing
Abstract: This talk explores the principles and challenges of U-Net, a foundational architecture in image processing. The first segment focuses on its mathematical foundations, explaining how the encoder-decoder framework enables efficient learning of sparse structural features by analyzing convolutional operations and transposed convolutions. The second segment addresses hallucination artifacts—plausible but inconsistent structures generated during inference. We present a distribution-free uncertainty quantification framework designed to quantitatively evaluate hallucination severity, emphasizing its potential for network architecture choices. Experimental comparisons between U-Net and SUNet, trained with L1 and L2 losses, reveal how variations in architecture and loss functions influence the nature of artifacts.
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Speaker Bio: Jianfei Li received the B.S. degree in mathematics from Ocean University of China, Qingdao, China, in 2017, the M.S. degree in mathematics from Sun Yat-sen University, Guangzhou, China, in 2020, and the Ph.D. degree in mathematics from City University of Hong Kong, Hong Kong, China. He is currently a Post-Doctoral fellow with the LMU Munich, Munich, Germany collaborating with Prof. Gitta Kutyniok. His research interests include approximation theory, signal processing, and deep neural networks.