Link to paper: A Recurrent Neural Network For Image Generation
This paper introduces a neural network architecture that generates realistic images sequentially. They also introduce a differentiable attention mechanism that allows the network to focus on local regions of the image during reconstruction. Main contributions:
- The network architecture is similar to other variational auto-encoders, except that
- The encoder and decoder are recurrent networks (LSTMs). The encoder's output is conditioned on the decoder's previous outputs, and the decoder's outputs are iteratively added to the resulting distribution from which images are generated.
- The spatial attention mechanism restricts the input region observed by the encoder and available to write for the decoder.
- The spatial soft attention mechanism is effective and fully differentiable, and can be used for other tasks.
- Images generated by DRAW look very realistic.