DRAW: A Recurrent Neural Network For Image Generation

1 Introduction

We draw pictures not at once, but in a sequencial, iterative fashion.This work proposes an architecture to create a scene in a time series, and refine the sketches successively.

The core of DRAW is a pair of recurrrent neural networks: an encoder that compresses the real images and a decoder that reconstitutes images after receiving codes. The loss function is a variational upper bound on the log-likelihood of the data. 

It generates images step by step , selectively attending to parts of images while ignoring others. 

The DRAW architecture is similar to other variational auto-encoders: an encoder network determines a distribution over latent codes that capture salient information about the input data; a decoder network receives samples from the code distribution and uses them to condition its own distribution over images.

2 The DRAW Network

2.1 Network Architecture 

Q(Zt|h(t,enc)) is a diagnonal Gaussian 

2.2 Loss Function

The final canvas matrix Ct is used to parameterise a model D(X|Ct). D is a Bernoulli distribution. 

 reconstruction loss

 latent loss

2.3 Stochastic data generation

2.4 Read and Write Operation

N*N grid of Gaussian Filters is positioned on the image by specifying the co-ordinates of the grid center and the stride distance between adjacent filters. 

(i,j)is a point in the attention patch, (a,b) is a point in the input image. 

イメージの各点がどれぐらいフィルターの一つの点Aに貢献するかを定量的に表現する重み行列。