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N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning
Our approach takes a larger `teacher' network as input and outputs a compressed `student' network derived from the `teacher' network.
Architecture Compression
A 1-D CNN encoder-decoder is trained to learn a mapping from discrete architecture space to a continuous embedding and back.
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