Multi-digit Image Synthesis Using Recurrent Conditional Variational Autoencoder

In the field of deep neural networks, several generative methods have been proposed to address the challenges from generative and discriminative tasks, e.g., natural language process, image caption and image generation. In this paper, a conditional recurrent variational autoencoder is proposed for multi-digit image synthesis. This model is capable of generating multi-digit images from the given number sequences and retaining the generalisation ability to recover different types of background. Our method is evaluated on SVHN dataset and the experimental results show it succeeds to generate multi-digit images with various styles according to the given sequential inputs. The generated images can also be easily identified by both human beings and convolutional neural networks for digit classification.