Neural Architecture Search with Reinforcement Learning
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Cites background from "Neural Architecture Search with Rei..."
...(NAS) [31] is an RNN–based learning model that can create a network for image classification....
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...For example, NASNet architecture was developed based on the NAS strategy by Zoph et al. [33]....
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...Dong et al. [35], introduced a NAS–based model to create an Adversarial neural network for medical image segmentation, such that the NAS technique is applied to create an optimal discriminator....
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...The NASNet strategy decreases the required computation by changing the search space....
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...Besides, NAS U–Net [36] obtained almost the similar results of the manual–designed networks, because, NAS U–Net also uses a unique network structure for segmentation....
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15 citations
Cites background from "Neural Architecture Search with Rei..."
..., importance sampling [24], evolutionary sampling [19], reinforcement learning-based sampling [41]....
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15 citations
15 citations
References
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"Neural Architecture Search with Rei..." refers methods in this paper
...Along with this success is a paradigm shift from feature designing to architecture designing, i.e., from SIFT (Lowe, 1999), and HOG (Dalal & Triggs, 2005), to AlexNet (Krizhevsky et al., 2012), VGGNet (Simonyan & Zisserman, 2014), GoogleNet (Szegedy et al., 2015), and ResNet (He et al., 2016a)....
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42,067 citations
31,952 citations
"Neural Architecture Search with Rei..." refers methods in this paper
...Along with this success is a paradigm shift from feature designing to architecture designing, i.e., from SIFT (Lowe, 1999), and HOG (Dalal & Triggs, 2005), to AlexNet (Krizhevsky et al., 2012), VGGNet (Simonyan & Zisserman, 2014), GoogleNet (Szegedy et al., 2015), and ResNet (He et al., 2016a)....
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