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CASS: Cross Adversarial Source Separation via Autoencoder
This paper introduces a cross adversarial source separation (CASS) framework
via autoencoder, a new model that aims at separating an input signal consisting
of a mixture of multiple components into individual components defined via
adversarial learning and autoencoder fitting. CASS unifies popular generative
networks like auto-encoders (AEs) and generative adversarial networks (GANs) in
a single framework. The basic building block that filters the input signal and
reconstructs the -th target component is a pair of deep neural networks
and as an encoder for dimension reduction and
a decoder for component reconstruction, respectively. The decoder
as a generator is enhanced by a discriminator network
that favors signal structures of the -th component in the
-th given dataset as guidance through adversarial learning. In contrast with
existing practices in AEs which trains each Auto-Encoder independently, or in
GANs that share the same generator, we introduce cross adversarial training
that emphasizes adversarial relation between any arbitrary network pairs
, achieving state-of-the-art performance
especially when target components share similar data structures
- …