Text-To-Image Generation using Generative Adversial Networks

Technology Stack – Python: TensorFlow, Tensorlayer, Numpy, Scipy
The project employs the use of Generative Adversarial Networks (GAN) to convert an input text into corresponding images.
The model was trained on VGG Flowers and UCSD Birds dataset. Also, every image has 10 captions each, relating to corresponding image in the dataset.
The text was converted into feature vectors using LSTM Embeddings. They were concatenated with images and were feed on to the network.
The model used Adam Optimizer to optimize the cost function of the Generative Network.