Explore the latest advancements in self-supervised deep learning for image recovery in this SIAM-IS Virtual Seminar Series talk. Delve into Hui Ji's research from the National University of Singapore, focusing on a dataset-free approach to solving ill-posed inverse problems in image reconstruction. Learn about Bayesian deep networks and their application in compressive sensing, comparing the performance of this self-supervised method to traditional non-learning techniques and supervised deep learning approaches. Gain insights into data augmentation, self-supervised loss functions, and the stability of training in this cutting-edge field. Examine experimental results demonstrating the method's effectiveness in reconstructing images from limited and noisy measurements, and understand its potential to reduce the cost and complexity of creating labeled training datasets for image recovery tasks.