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Text To Image Synthesis Using Thought Vectors

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This is an experimental tensorflow implementation of synthesizing images from captions using Skip Thought Vectors. The images are synthesized using the GAN-CLS Algorithm from the paper Generative Adversarial Text-to-Image Synthesis. This implementation is built on top of the excellent DCGAN in Tensorflow. The following is the model architecture. The blue bars represent the Skip Thought Vectors for the captions.

Model architecture

Image Source : Generative Adversarial Text-to-Image Synthesis Paper

Requirements

Datasets

Usage

python data_loader.py --data_set="flowers"

Sample Images Generated

Following are the images generated by the generative model from the captions.

CaptionGenerated Images
the flower shown has yellow anther red pistil and bright red petals
this flower has petals that are yellow, white and purple and has dark lines
the petals on this flower are white with a yellow center
this flower has a lot of small round pink petals.
this flower is orange in color, and has petals that are ruffled and rounded.
the flower has yellow petals and the center of it is brown

Implementation Details

Pre-trained Models

TODO

References

Alternate Implementations

License

MIT