Generating Image Sequence from Description with LSTM Conditional GAN.
2018 24th International Conference on Pattern Recognition (ICPR)(2018)
IIT
Abstract
Generating images from word descriptions is a challenging task. Generative adversarial networks(GANs) are shown to be able to generate realistic images of real-life objects. In this paper, we propose a new neural network architecture of LSTM Conditional Generative Adversarial Networks to generate images of real-life objects. Our proposed model is trained on the Oxford-102 Flowers and Caltech-UCSD Birds-200-2011 datasets. We demonstrate that our proposed model produces the better results surpassing other state-of-art approaches.
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Key words
image sequence,Caltech-UCSD Birds-200-2011 datasets,LSTM Conditional Generative Adversarial Networks,neural network architecture,LSTM Conditional GAN
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