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Generating images from word descriptions is a challenging task. Generativeadversarial networks(GANs) are shown to be able to generate realistic images ofreal-life objects. In this paper, we propose a new neural network architectureof LSTM Conditional Generative Adversarial Networks to generate images ofreal-life objects. Our proposed model is trained on the Oxford-102 Flowers andCaltech-UCSD Birds-200-2011 datasets. We demonstrate that our proposed modelproduces the better results surpassing other state-of-art approaches.

Generating Image Sequence from Description with LSTM Conditional GAN