Fixing GAN Optimization Through Competitive Gradient Descent - Anima Anandkumar

Explore the intricacies of GAN optimization through competitive gradient descent in this 35-minute lecture by Anima Anandkumar from Caltech. Delve into topics such as single agent optimization, competitive optimization, strategic equilibria, and applications in machine learning. Examine the concept of alternating gradient descent and learn how to linearize a game. Understand the principles of competitive gradient descent, compare it to existing methods, and investigate the solution of a GAN. Analyze different models of competing agents, including global, myopic, and predictive games. Conclude with a review of numerical results in this comprehensive talk from the Workshop on Theory of Deep Learning at the Institute for Advanced Study.