ProjectAdversarialDenoising

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

ProjectAdversarialDenoising

[[][Adversarial Learning for Denoising]]

Scientific Director: Vasileios Belagiannis

Contact Person(s): Leslie Casas

Abstract

We present the idea of adversarial learning for one-dimensional signal denoising. An adversarial encoder-decoder network is proposed for denoising signals, represented by a sequence of measurements. We rely on the network’s latent representation as input to the discriminator to classify the signal into clean or noisy. Aligning the noisy and clean signal distributions results in removing the signal noise. Unlike the standard GAN training, we propose a new formulation that suits to one-dimensional signal denoising. In the evaluation, we show better performance than the related approaches, such as autoencoders and recurrent neural networks, demonstrating the benefits of our adversarial encoder-decoder network in different signal and noise types.

Team

Contact Person(s)

Location



Technische Universität München
Institut für Informatik / I16
Boltzmannstr. 3
85748 Garching bei München

Tel.: +49 89 289-17058
Fax: +49 89 289-17059
Visit our lab at Garching.



[[][internal project page]]

Please contact Leslie Casas for available student projects within this research project.

Edit | Attach | Refresh | Diffs | More | Revision r1.1 - 17 Dec 2018 - 14:14 - LeslieCasas

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