R Deep Learning Essentials - Second Edition

Denoising auto-encoders

Denoising auto-encoders remove noise or denoise data, and are a useful technique for learning a latent representation of raw data (Vincent, P., Larochelle, H., Bengio, Y., and Manzagol, P. A. (2008, July); Bengio, Y.,Courville, A., and Vincent, P. (2013)). We said that the general task of an auto-encoder was to optimize: F(x, g(f(x))). However, for a denoising auto-encoder, the task is to recover x from a noisy or corrupted version of x. One application of denoising auto-encoders is to restore old images that may be blurred or corrupted.

Although denoising auto-encoders are used to try and recover the true representation from corrupted data or data with noise, this technique can also be used as a regularization tool. ...