Deep convolutional and recurrent writer

This paper proposes a new architecture Deep Convolutional and Recurrent writer (DCRW) for image generation by adapting the deep Recurrent attentive writer (DRAW) architecture which is a sequential variational auto-encoder with a sequential attention mechanism for image generation. The main difference between DRAW and DCRW is that in DCRW we have replaced RNN in encoder with CNN and after replacement attention mechanism have been used for CNN. The reason behind this modification is that CNNs are the state of the art for image processing in deep learning and their basic architecture is inspired from the visual cortex. Further, for the testing of proposed architecture experiments are performed on MNIST handwritten digits data set for generation of images and results are analyzed.