Generative Restricted Kernel Machines: A Framework for Multi-view Generation and Disentangled Feature Learning
This work addresses the challenge of generating coherent data from multiple perspectives while learning disentangled representations, which is incremental as it builds on existing kernel and neural network methods.
The paper tackles the problem of multi-view generation and disentangled feature learning by introducing Gen-RKM, a framework based on Restricted Kernel Machines that enables joint generation from multiple data views and learns uncorrelated features, with experiments showing qualitative and quantitative improvements on standard datasets.
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.