A genetic programming approach to designing convolutional neural network architectures
Masanori Suganuma,Shinichi Shirakawa,Tomoharu Nagao +2 more
- pp 497-504
TLDR
This paper attempts to automatically construct CNN architectures for an image classification task based on Cartesian genetic programming (CGP), and shows that the proposed method can be used to automatically find the competitive CNN architecture compared with state-of-the-art models.Abstract:
The convolutional neural network (CNN), which is one of the deep learning models, has seen much success in a variety of computer vision tasks. However, designing CNN architectures still requires expert knowledge and a lot of trial and error. In this paper, we attempt to automatically construct CNN architectures for an image classification task based on Cartesian genetic programming (CGP). In our method, we adopt highly functional modules, such as convolutional blocks and tensor concatenation, as the node functions in CGP. The CNN structure and connectivity represented by the CGP encoding method are optimized to maximize the validation accuracy. To evaluate the proposed method, we constructed a CNN architecture for the image classification task with the CIFAR-10 dataset. The experimental result shows that the proposed method can be used to automatically find the competitive CNN architecture compared with state-of-the-art models.read more
Citations
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A survey of the recent architectures of deep convolutional neural networks
TL;DR: Deep Convolutional Neural Networks (CNNs) as mentioned in this paper are a special type of Neural Networks, which has shown exemplary performance on several competitions related to Computer Vision and Image Processing.
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AutoML: A survey of the state-of-the-art
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Regularized Evolution for Image Classifier Architecture Search
TL;DR: This work evolves an image classifier---AmoebaNet-A---that surpasses hand-designs for the first time and gives evidence that evolution can obtain results faster with the same hardware, especially at the earlier stages of the search.
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Proceedings Article
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