An autoencoder is a neural network often used to learn an efficient representation of input data (typically in a reduced dimension) in an unsupervised way. A denoising autoencoder is a stochastic version of an autoencoder that takes (similar) inputs corrupted by noise and is trained to recover the original inputs (typically using some deep learning library functions) in order to obtain a good representation. We can use denoising autoencoders to learn robust representations from a set of similar input images (corrupted with noise) and then generate the denoised images.
Image denoising with a denoising autoencoder
Getting ready
We will be using the labeled faces in the wild (lfw) face dataset from scikit-learn (it contains face...
