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Generalizing GANs: A Turing Perspective
Roderich Gross · Yue Gu · Wei Li · Melvin Gauci

Wed Dec 06 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #102 #None

Recently, a new class of machine learning algorithms has emerged, where models and discriminators are generated in a competitive setting. The most prominent example is Generative Adversarial Networks (GANs). In this paper we examine how these algorithms relate to the famous Turing test, and derive what - from a Turing perspective - can be considered their defining features. Based on these features, we outline directions for generalizing GANs - resulting in the family of algorithms referred to as Turing Learning. One such direction is to allow the discriminators to interact with the processes from which the data samples are obtained, making them "interrogators", as in the Turing test. We validate this idea using two case studies. In the first case study, a computer infers the behavior of an agent while controlling its environment. In the second case study, a robot infers its own sensor configuration while controlling its movements. The results confirm that by allowing discriminators to interrogate, the accuracy of models is improved.

Author Information

Roderich Gross (The University of Sheffield)

Roderich Gross is a Senior Lecturer in the Department of Automatic Control and Systems Engineering at the University of Sheffield and an Executive Committee Member of Sheffield Robotics, where he leads the Enabling Technologies theme. He received a Ph.D. degree in engineering science in 2007 from Université libre de Bruxelles in 2007, and was a JSPS Fellow (Tokyo Institute of Technology) and a Marie Curie Fellow (EPFL & Unilever). He has authored more than 70 publications in robotics and artificial intelligence. He has made contributions to the coordination of swarm and reconfigurable robots, and invented a machine learning method called Turing Learning. Dr Gross serves as the General Chair of DARS 2016, Editor of IROS 2015-17, and as an Associate Editor of Swarm Intelligence, IEEE Robotics and Automation Letters, and IEEE Computational Intelligence Magazine.

Yue Gu (The University of Sheffield)
Wei Li (University of York)
Melvin Gauci (Harvard University)

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