Mastering Machine Learning Algorithms

Denoising autoencoders

Autoencoders can be used to determine under-complete representations of a dataset; however, Bengio et al. (in P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P. Manzagol's book Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion, from the Journal of Machine Learning Research 11/2010) proposed to use them not to learn the exact representation of a sample in order to rebuild it from a low-dimensional code, but rather to denoise input samples. This is not a brand new idea, because, for example, Hopfield networks (proposed a few decades ago) had the same purpose, but its limitations in terms of capacity led researchers to look for different methods. Nowadays, ...