Generative Adversarial Text to Image Synthesis

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Authors Scott Reed, Xinchen Yan, Lajanugen Logeswaran, Zeynep Akata, Honglak Lee, Bernt Schiele
Journal/Conference Name 33rd International Conference on Machine Learning, ICML 2016
Paper Category
Paper Abstract Automatic synthesis of realistic images from text would be interesting and useful, but current AI systems are still far from this goal. However, in recent years generic and powerful recurrent neural network architectures have been developed to learn discriminative text feature representations. Meanwhile, deep convolutional generative adversarial networks (GANs) have begun to generate highly compelling images of specific categories, such as faces, album covers, and room interiors. In this work, we develop a novel deep architecture and GAN formulation to effectively bridge these advances in text and image model- ing, translating visual concepts from characters to pixels. We demonstrate the capability of our model to generate plausible images of birds and flowers from detailed text descriptions.
Date of publication 2016
Code Programming Language Multiple
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