Predicting the Future Using Deep Adversarial Networks

Predicting the Future Using Deep Adversarial Networks: Learning With No Labeled Data: Labeling data to solve a certain task can be expensive, slow and does not scale. If unsupervised learning works, then one can have very little labelled data to help a machine solve a particular task. Most traditional unsupervised learning methods such as PCA and K-means clustering do not work well for complicated data distributions, making them useless for a lot of tasks. In this talk, I’ll go over recent advances in a technique for unsupervised learning called Generative Adversarial networks, which can learn to generate very complicated data distributions such as images and videos. These trained adversarial networks are then used to solve new tasks with very little labeled data, making them an attractive class of algorithms for many domains where there is limited labeled data but unlimited unlabeled data.

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