Analysis of an Adversarial Approach to Blind Source Separation
Juan Manuel Espinoza Bullón, Romis Ribeiro Attux, Levy Boccato

DOI: 10.14209/SBRT.2020.1570658229
Evento: XXXVIII Simpósio Brasileiro de Telecomunicações e Processamento de Sinais (SBrT2020)
Keywords: Blind Source Separation Independent Component Analysis Adversarial Learning Autoencoders
In this work, we analyze the adversarial network proposed by Brakel and Bengio at [5] to solve the problem of independent component analysis (ICA). Guided by a discriminator of independence, a linear autoencoder learns to codify a set of samples into estimates of their independent components. This is achieved by training the autoencoder to "fool the discriminator" and generate a code of latent variables. The present study focuses on linear mixtures, having the JADE and FastICA algorithms as benchmarks, but the paradigm has a significant potential of extension to more general scenarios.