Model-Based Deep Learning: Deep Equilibrium Models and Proximal Gradient Maps
Explore advanced concepts in deep learning and signal processing through this webinar featuring Prof. Mathews Jacob from the University of Iowa. Delve into model-based deep learning, deep equilibrium models, and their applications in various fields. Examine standard propagation techniques and their limitations, then discover alternative approaches such as proximal gradient maps and monotone operators. Investigate the importance of adversarial robustness, unique fixed points, and safety in deep learning models. Learn about undersample data challenges and solutions, including deep image priors and deep generator models. Gain insights into practical applications in multislice imaging, speech processing, and MRI, with a focus on motion-compensated image recovery techniques.