Progressive Neural Architecture Search

ECCV 2018 • Chenxi Liu • Barret Zoph • Maxim Neumann • Jonathon Shlens • Wei Hua • Li-Jia Li • Li Fei-Fei • Alan Yuille • Jonathan Huang • Kevin Murphy

We propose a new method for learning the structure of convolutional neural networks (CNNs) that is more efficient than recent state-of-the-art methods based on reinforcement learning and evolutionary algorithms. Our approach uses a sequential model-based optimization (SMBO) strategy, in which we search for structures in order of increasing complexity, while simultaneously learning a surrogate model to guide the search through structure space... (read more)

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#26 best model for Image Classification on ImageNet

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK COMPARE
Image Classification ImageNet PNASNet-5 Top 1 Accuracy 82.9% # 26
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Image Classification ImageNet PNASNet-5 Top 5 Accuracy 96.2% # 22
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Image Classification ImageNet PNASNet-5 Number of params 86.1M # 1
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