Unlearned Neural Networks as Image Priors for Inverse Problems

Explore unlearned neural networks as image priors for inverse problems in imaging through this 50-minute online lecture from Northeastern University's CS 7180 Spring 2020 class. Delve into Deep Image Prior, Deep Decoder, and Deep Geometric Prior concepts, examining their applications in super-resolution and denoising. Analyze the architectural differences, parameter considerations, and representation capabilities of these approaches. Investigate the geometric picture, smoothness locality, and over/under-parameterization effects. Gain insights from related papers on Image Adaptive GAN and Latent Convolutional Models. Access accompanying lecture notes for a comprehensive understanding of these cutting-edge techniques in artificial intelligence and computer vision.