Neural Network Architectures for Images

Explore neural network architectures for image processing in this comprehensive 47-minute lecture. Delve into tasks such as classification, segmentation, denoising, blind deconvolution, superresolution, and inpainting. Examine various architectures including Multilayer Perceptrons, Convolutional Neural Networks, Residual Nets, Encoder-decoder nets, and autoencoders. Learn about the concept of sparing networks from learning already known information. Cover topics like LexNet, VGGNet, CNNs, Residual Blocks, Encoder-Decoder Units, Compressive Autoencoders, and MRI reconstruction. Access accompanying lecture notes for further study and explore referenced research papers for in-depth understanding of the field.

Introduction
Multilayer Perceptrons
LexNet
VGGNet
CNNs
Examples
Residual Blocks
SuperResolution
EncoderDecoder
Unit
Autoencoders
Compressive Autoencoders
superresolution autoencoders
sparing nets
MRI reconstruction