Generative Restricted Kernel Machines: A framework for multi-view generation and disentangled feature learning. (March 2021)

• Record Type: Journal Article
• Authors: Pandey, Arun; Schreurs, Joachim; Suykens, Johan A.K.
• Is Part Of: Neural networks. Volume 135(2021)
• Page Start: 177
• Page End: 191
• Publication Date: 2021-03
• Subjects: Generative models -- Latent variable models -- Deep learning -- Restricted kernel machines; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2020.12.010
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: This paper introduces a novel framework for generative models based on Restricted Kernel Machines (RKMs) with joint multi-view generation and uncorrelated feature learning, called Gen-RKM. To enable joint multi-view generation, this mechanism uses a shared representation of data from various views. Furthermore, the model has a primal and dual formulation to incorporate both kernel-based and (deep convolutional) neural network based models within the same setting. When using neural networks as explicit feature-maps, a novel training procedure is proposed, which jointly learns the features and shared subspace representation. The latent variables are given by the eigen-decomposition of the kernel matrix, where the mutual orthogonality of eigenvectors represents the learned uncorrelated features. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of generated samples on various standard datasets.