Generative adversarial networks for non-negative matrix factorization in temporal psycho-visual modulation

Abstract The image factorization problem is the key challenge in Temporal Psycho-Visual Modulation (TPVM). In this paper, we present an end-to-end learned model for image-based non-negative matrix factorization. We decompose a set of images into a small number of image bases which can be used to reconstruct all the images by linearly combining the bases. During the process, the image bases, as well as their weights for the linear combination, are unknown. The method is based on conditional GAN and a variational sample disturber. Traditional NMF methods suffer from slow computational speed and poor generalization ability. A deep neural network shows potential in these two aspects. We conduct adversarial training in our model to generate better bases for image restoration. Our method outperforms other CNN based methods on several public datasets. Compared to traditional NMF algorithms, our model generates image bases that preserve details. Our model has advantages in speed as well as generalization ability.