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DRAW: A Recurrent Neural Network For Image Generation

Authors: Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra

Link: https://arxiv.org/pdf/1502.04623.pdf

Problem Statement & Main Contributions

  • The paper presents a sequential VAE with each cell as an LSTM, with the decoder incrementally adding the outputs with a attention update mechanism for restricting the input area by the encoder and the output update area by the decoder.

  • The model is tested on MNIST ,SVHN and CIFAR0-10 dataset.

Related Work (Key Papers)

  • Previously proposed ideas are based either on VAEs or on generative modelling approach which generate images in a single pass.

Assumptions and Future Work

  • The model could have been tested against the performance of GANs and other generative models available. Also, the model when tested against the CIFAR-10 dataset doesnot seem to perform upto mark. These could be some of the potential areas for future work.