TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK
Image Classification	ImageNet	PNASNet-5	Top 1 Accuracy	82.9%	# 48
Image Classification	ImageNet	PNASNet-5	Top 5 Accuracy	96.2%	# 28
Image Classification	ImageNet	PNASNet-5	Number of params	86.1M	# 25

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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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IMAGE CLASSIFICATION

NEURAL ARCHITECTURE SEARCH

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Ranked #48 on Image Classification on ImageNet

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TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK	BENCHMARK
Image Classification	ImageNet	PNASNet-5	Top 1 Accuracy	82.9%	# 48	Compare
			Top 5 Accuracy	96.2%	# 28	Compare
			Number of params	86.1M	# 25	Compare

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METHOD	TYPE
Dense Connections	Feedforward Networks
Feedforward Network	Feedforward Networks
Softmax	Output Functions
Depthwise Convolution	Convolutions
Pointwise Convolution	Convolutions
Depthwise Separable Convolution	Convolutions
Convolution	Convolutions
Max Pooling	Pooling Operations
RMSProp	Stochastic Optimization
PNAS	Neural Architecture Search

Progressive Neural Architecture Search

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