Net2Net: Accelerating Learning via Knowledge Transfer

author: Tianqi Chen, Department of Computer Science and Engineering, University of Washington
published: May 27, 2016,   recorded: May 2016,   views: 4081
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Description

We introduce techniques for rapidly transferring the information stored in one neural net into another neural net. The main purpose is to accelerate the training of a significantly larger neural net. During real-world workflows, one often trains very many different neural networks during the experimentation and design process. This is a wasteful process in which each new model is trained from scratch. Our Net2Net technique accelerates the experimentation process by instantaneously transferring the knowledge from a previous network to each new deeper or wider network. Our techniques are based on the concept of function-preserving transformations between neural network specifications. This differs from previous approaches to pre-training that altered the function represented by a neural net when adding layers to it. Using our knowledge transfer mechanism to add depth to Inception modules, we demonstrate a new state of the art accuracy rating on the ImageNet dataset.

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Download slides icon Download slides: iclr2016_chen_net2net_01.pdf (752.7┬áKB)


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