Generative Adversarial Networks
Generative Adversarial Networks, or GANs for short, are an approach to generative modeling using deep learning methods, such as convolutional neural networks.
Generative modeling is an unsupervised learning task in machine learning that involves automatically discovering and learning the regularities or patterns in input data in such a way that the model can be used to generate or output new examples that plausibly could have been drawn from the original dataset.
Progress in GANs
There has been tremendous improvements in the way GANs are used for different problem statements, they include but not limited to
GANs in Speech to Facial Animation
The aim is to syntheisize realistic frames of face image having lip synchronization with the given speech. This generative task can be approached effectively by utilizing the power of GANs.