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Poster
Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
Anh Nguyen · Alexey Dosovitskiy · Jason Yosinski · Thomas Brox · Jeff Clune

Mon Dec 05 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #66
in Monday Posters »

Deep neural networks (DNNs) have demonstrated state-of-the-art results on many pattern recognition tasks, especially vision classification problems. Understanding the inner workings of such computational brains is both fascinating basic science that is interesting in its own right---similar to why we study the human brain---and will enable researchers to further improve DNNs. One path to understanding how a neural network functions internally is to study what each of its neurons has learned to detect. One such method is called activation maximization, which synthesizes an input (e.g. an image) that highly activates a neuron. Here we dramatically improve the qualitative state of the art of activation maximization by harnessing a powerful, learned prior: a deep generator network. The algorithm (1) generates qualitatively state-of-the-art synthetic images that look almost real, (2) reveals the features learned by each neuron in an interpretable way, (3) generalizes well to new datasets and somewhat well to different network architectures without requiring the prior to be relearned, and (4) can be considered as a high-quality generative method (in this case, by generating novel, creative, interesting, recognizable images).

Keywords: Deep Learning or Neural Networks · (Application) Computer Vision · (Application) Object and Pattern Recognition · (Cognitive/Neuroscience) Neural Coding
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Author Information

Anh Nguyen (University of Wyoming)
Alexey Dosovitskiy (University of Freiburg)
Jason Yosinski (Cornell)

Dr. Jason Yosinski is a machine learning researcher, was a founding member of Uber AI Labs, and is scientific adviser to Recursion Pharmaceuticals and several other companies. His work focuses on building more capable and more understandable AI. As scientists and engineers build increasingly powerful AI systems, the abilities of these systems increase faster than does our understanding of them, motivating much of his work on AI Neuroscience: an emerging field of study that investigates fundamental properties and behaviors of AI systems. Dr. Yosinski completed his PhD as a NASA Space Technology Research Fellow working at the Cornell Creative Machines Lab, the University of Montreal, Caltech/NASA Jet Propulsion Laboratory, and Google DeepMind. His work on AI has been featured on NPR, Fast Company, the Economist, TEDx, XKCD, and on the BBC. Prior to his academic career, Jason cofounded two web technology companies and started a program in the Los Angeles school district that teaches students algebra via hands-on robotics. In his free time, Jason enjoys cooking, sailing, motorcycling, reading, paragliding, and sometimes pretending he's an artist.

Thomas Brox (University of Freiburg)
Jeff Clune (University of Wyoming)

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