2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDMLSP-20.5
Paper Title A QUATERNION-VALUED VARIATIONAL AUTOENCODER
Authors Eleonora Grassucci, Danilo Comminiello, Aurelio Uncini, Sapienza University of Rome, Italy
SessionMLSP-20: Attention and Autoencoder Networks
LocationGather.Town
Session Time:Wednesday, 09 June, 15:30 - 16:15
Presentation Time:Wednesday, 09 June, 15:30 - 16:15
Presentation Poster
Topic Machine Learning for Signal Processing: [MLR-DEEP] Deep learning techniques
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Deep probabilistic generative models have achieved incredible success in many fields of application. Among such models, variational autoencoders (VAEs) have proved their ability in modeling a generative process by learning a latent representation of the input. In this paper, we propose a novel VAE defined in the quaternion domain, which exploits the properties of quaternion algebra to improve performance while significantly reducing the number of parameters required by the network. The success of the proposed quaternion VAE with respect to traditional VAEs relies on the ability to leverage the internal relations between quaternion-valued input features and on the properties of second-order statistics which allow to define the latent variables in the augmented quaternion domain. In order to show the advantages due to such properties, we define a plain convolutional VAE in the quaternion domain and we evaluate its performance with respect to its real-valued counterpart on the CelebA face dataset.