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SequenceGan text to image synthesis with Seq models and GANs
  • Yigit Gunduc
Yigit Gunduc
Ankara University Development Foundation Private High School, Ankara University Development Foundation Private High School, Ankara University Development Foundation Private High School

Corresponding Author:[email protected]

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Abstract

Generative Adversarial Nets are one of the most popular generative frameworks. In our work, we introduce the SequenceGAN, a method that can generate images based on a given caption by supplying the conditional sequential text input to the generator and the discriminator. Unlike other conditional methods, SequenceGAN uses recurrent layers for better context understanding. We also demonstrated SequenceGANs performance by applying it to the MNIST and Flickr 8k datasets.