A Generative Adversarial Network (GAN) is a class of models consisting of two neural networks, a generator, and a discriminator, which are trained simultaneously through adversarial training. The generator creates synthetic data, and the discriminator evaluates whether the generated data is real or fake. The goal is for the generator to produce data that is indistinguishable from real data, while the discriminator improves its ability to differentiate between real and generated data.


Video Generation From Text

Generating videos from text has proven to be a significant challenge for existing generative models. We tackle this problem by training a conditional generative model to extract both static and dynamic information from text. This is manifested in a hybrid framework, employing a Variational Autoencoder (VAE) and a Generative Adversarial Network (GAN). The static features, called “gist,” are used to sketch text-conditioned background color and object layout structure. Dynamic features are considered by transforming input text into an image filter. To obtain a large amount of data for training the deep-learning model, we develop a method to automatically create a matched text-video corpus from publicly available online videos. Experimental results show that the proposed framework generates plausible and diverse short-duration smooth videos, while accurately reflecting the input text information. It significantly outperforms baseline models that directly adapt text-to-image generation procedures to produce videos. Performance is evaluated both visually and by adapting the inception score used to evaluate image generation in GANs.