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论文类型：期刊论文

发表时间：2020-02-01

发表刊物：JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION

收录刊物：EI、SCIE

卷号：67

ISSN号：1047-3203

关键字：Variational autoencoder; Matrix Gaussian distribution; Variational inference; Face completion; Image denoising

摘要：Variational Auto-Encoder (VAE) is an important probabilistic technology to model 1D vectorial data. However, when applying VAE model to 2D image, vectorization is necessary. Vectorization process may lead to dimension curse and lose valuable spatial information. To avoid these problems, we propose a novel VAE model based on matrix variables named as Matrix-variate Variational Auto-Encoder (MVVAE). In this model, input, hidden and latent variables are all in matrix form, therefore inherent spatial structure of 2D images can be maintained and utilized better. Especially, the latent variable is assumed to follow matrix Gaussian distribution which is more suitable for describing 2D images. To solve the weights and the posterior of latent variable, the variational inference process is given. The experiments are designed for three real-world application: reconstruction, denoising and completion. The experimental results demonstrate that MVVAE shows better performance than VAE and other probabilistic methods for modeling and processing 2D data. (C) 2020 Elsevier Inc. All rights reserved.