Enhanced Gradient for Differentiable Architecture Search Article Swipe

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Haichao Zhang , Kuangrong Hao , Lei Gao , Xue‐song Tang , Bing Wei ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2103.12529

In recent years, neural architecture search (NAS) methods have been proposed for the automatic generation of task-oriented network architecture in image classification. However, the architectures obtained by existing NAS approaches are optimized only for classification performance and do not adapt to devices with limited computational resources. To address this challenge, we propose a neural network architecture search algorithm aiming to simultaneously improve network performance (e.g., classification accuracy) and reduce network complexity. The proposed framework automatically builds the network architecture at two stages: block-level search and network-level search. At the stage of block-level search, a relaxation method based on the gradient is proposed, using an enhanced gradient to design high-performance and low-complexity blocks. At the stage of network-level search, we apply an evolutionary multi-objective algorithm to complete the automatic design from blocks to the target network. The experiment results demonstrate that our method outperforms all evaluated hand-crafted networks in image classification, with an error rate of on CIFAR10 and an error rate of on CIFAR100, both at network parameter size less than one megabit. Moreover, compared with other neural architecture search methods, our method offers a tremendous reduction in designed network architecture parameters.

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Computer science Network architecture Block (permutation group theory) Artificial intelligence Architecture Artificial neural network Contextual image classification Search algorithm Reduction (mathematics) Word error rate Data mining Pattern recognition (psychology) Machine learning Image (mathematics) Algorithm Mathematics Computer network Art Visual arts Geometry

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2103.12529
PDF: https://arxiv.org/pdf/2103.12529
OA Status: green
References: 45
Related Works: 10
OpenAlex ID: https://openalex.org/W3136246742

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DOI: https://doi.org/10.48550/arxiv.2103.12529

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Title: Enhanced Gradient for Differentiable Architecture Search

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Type: preprint

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Language: en

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Publication year: 2021

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Publication date: 2021-03-23

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Authors: Haichao Zhang, Kuangrong Hao, Lei Gao, Xue‐song Tang, Bing Wei

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Landing page: https://arxiv.org/abs/2103.12529

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Concepts: Computer science, Network architecture, Block (permutation group theory), Artificial intelligence, Architecture, Artificial neural network, Contextual image classification, Search algorithm, Reduction (mathematics), Word error rate, Data mining, Pattern recognition (psychology), Machine learning, Image (mathematics), Algorithm, Mathematics, Computer network, Art, Visual arts, Geometry

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References (count): 45

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abstract_inverted_index.propose	51
abstract_inverted_index.results	137
abstract_inverted_index.search,	92, 117
abstract_inverted_index.search.	86
abstract_inverted_index.stages:	81
abstract_inverted_index.However,	22
abstract_inverted_index.compared	174
abstract_inverted_index.complete	125
abstract_inverted_index.designed	188
abstract_inverted_index.enhanced	104
abstract_inverted_index.existing	27
abstract_inverted_index.gradient	99, 105
abstract_inverted_index.megabit.	172
abstract_inverted_index.methods,	180
abstract_inverted_index.network.	134
abstract_inverted_index.networks	146
abstract_inverted_index.obtained	25
abstract_inverted_index.proposed	10, 72
abstract_inverted_index.CIFAR100,	163
abstract_inverted_index.Moreover,	173
abstract_inverted_index.accuracy)	66
abstract_inverted_index.algorithm	57, 123
abstract_inverted_index.automatic	13, 127
abstract_inverted_index.evaluated	144
abstract_inverted_index.framework	73
abstract_inverted_index.optimized	31
abstract_inverted_index.parameter	167
abstract_inverted_index.proposed,	101
abstract_inverted_index.reduction	186
abstract_inverted_index.approaches	29
abstract_inverted_index.challenge,	49
abstract_inverted_index.experiment	136
abstract_inverted_index.generation	14
abstract_inverted_index.relaxation	94
abstract_inverted_index.resources.	45
abstract_inverted_index.tremendous	185
abstract_inverted_index.block-level	82, 91
abstract_inverted_index.complexity.	70
abstract_inverted_index.demonstrate	138
abstract_inverted_index.outperforms	142
abstract_inverted_index.parameters.	191
abstract_inverted_index.performance	35, 63
abstract_inverted_index.architecture	4, 18, 55, 78, 178, 190
abstract_inverted_index.evolutionary	121
abstract_inverted_index.hand-crafted	145
abstract_inverted_index.architectures	24
abstract_inverted_index.automatically	74
abstract_inverted_index.computational	44
abstract_inverted_index.network-level	85, 116
abstract_inverted_index.task-oriented	16
abstract_inverted_index.classification	34, 65
abstract_inverted_index.low-complexity	110
abstract_inverted_index.simultaneously	60
abstract_inverted_index.classification,	149
abstract_inverted_index.classification.	21
abstract_inverted_index.multi-objective	122
abstract_inverted_index.high-performance	108
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	5
sustainable_development_goals[0].id	https://metadata.un.org/sdg/9
sustainable_development_goals[0].score	0.5199999809265137
sustainable_development_goals[0].display_name	Industry, innovation and infrastructure
citation_normalized_percentile