Regularized Evolution for Image Classifier Architecture Search
Open accessAbstract
The effort devoted to hand-crafting neural network image classifiers has motivated the use of architecture search to discover them automatically. Although evolutionary algorithms have been repeatedly applied to neural network topologies, the image classifiers thus discovered have remained inferior to human-crafted ones. Here, we evolve an image classifier— AmoebaNet-A—that surpasses hand-designs for the first time. To do this, we modify the tournament selection evolutionary algorithm by introducing an age property to favor the younger genotypes. Matching size, AmoebaNet-A has comparable accuracy to current state-of-the-art ImageNet models discovered with more complex architecture-search methods. Scaled to larger size, AmoebaNet-A sets a new state-of-theart 83.9% top-1 / 96.6% top-5 ImageNet accuracy. In a controlled comparison against a well known reinforcement learning algorithm, we give evidence that evolution can obtain results faster with the same hardware, especially at the earlier stages of the search. This is relevant when fewer compute resources are available. Evolution is, thus, a simple method to effectively discover high-quality architectures.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Xception: Deep Learning with Depthwise Separable Convolutions
Chollet, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.195 · Cited by 20,380 · see citing works
Zagoruyko, S., & Komodakis, N. (2016) · Procedings of the British Machine Vision Conference 2016 · doi:10.5244/c.30.87 · Cited by 7,244 · see citing works
Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011) · Psychological Science · doi:10.1177/0956797611417632 · Cited by 6,567 · see citing works
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11231 · Cited by 4,636 · see citing works
Deep Reinforcement Learning That Matters
Henderson, P., Islam, R., Bachman, P., Pineau, J., Precup, D., & Meger, D. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11694 · Cited by 1,685 · see citing works
Flagged first, then by citations
Research works printed but not linked: 40
- Angeline, P. J.; Saunders, G. M.; and Pollack, J. B. 1994. An evolutionary algorithm that constructs recurrent neural networks. IEEE transactions on Neural Networks. 2 https://colab.research.google.com/github/google-research/ google-research/blob/master/evolution/regularized evolution algorithm/regularized evolution.ipynb 3 https://tfhub.dev/google/imagenet/amoebanet a n18 f448/ classification/1
- Baker, B.; Gupta, O.; Naik, N.; and Raskar, R. 2017a. Designing neural network architectures using reinforcement learning. In ICLR.
- Baker, B.; Gupta, O.; Raskar, R.; and Naik, N. 2017b. Ac- celerating neural architecture search using performance pre- diction. ICLR Workshop.
- Bergstra, J., and Bengio, Y. 2012. Random search for hyper- parameter optimization. JMLR.
- Brock, A.; Lim, T.; Ritchie, J. M.; and Weston, N. 2018. Smash: one-shot model architecture search through hyper- networks. In ICLR.
- Cai, H.; Chen, T.; Zhang, W.; Yu, Y.; and Wang, J. 2018. Efficient architecture search by network transformation. In AAAI.
- Chen, Y.; Li, J.; Xiao, H.; Jin, X.; Yan, S.; and Feng, J. 2017. Dual path networks. In NIPS.
- Ciregan, D.; Meier, U.; and Schmidhuber, J. 2012. Multi- column deep neural networks for image classification. In CVPR.
- Coleman, C.; Kang, D.; Narayanan, D.; Nardi, L.; Zhao, T.; Zhang, J.; Bailis, P.; Olukotun, K.; Re, C.; and Zaharia, M. 2018. Analysis of dawnbench, a time-to-accuracy machine learning performance benchmark. arXiv preprint arXiv:1806.01427. · arXiv
- Cortes, C.; Gonzalvo, X.; Kuznetsov, V.; Mohri, M.; and Yang, S. 2017. Adanet: Adaptive structural learning of arti- ficial neural networks. In ICML.
- Cubuk, E. D.; Zoph, B.; Mane, D.; Vasudevan, V.; and Le, Q. V. 2018. Autoaugment: Learning augmentation policies from data. arXiv.
- Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; and Fei- Fei, L. 2009. Imagenet: A large-scale hierarchical image database. In CVPR.
- Domhan, T.; Springenberg, J. T.; and Hutter, F. 2017. Speed- ing up automatic hyperparameter optimization of deep neu- ral networks by extrapolation of learning curves. In IJCAI.
- Elsken, T.; Metzen, J.-H.; and Hutter, F. 2017. Simple and efficient architecture search for convolutional neural net- works. ICLR Workshop.
- Elsken, T.; Metzen, J. H.; and Hutter, F. 2018. Neural archi- tecture search: A survey. arXiv.
- Fahlman, S. E., and Lebiere, C. 1990. The cascade- correlation learning architecture. In NIPS.
- Feurer, M.; Klein, A.; Eggensperger, K.; Springenberg, J.; Blum, M.; and Hutter, F. 2015. Efficient and robust auto- mated machine learning. In NIPS.
- Hu, J.; Shen, L.; and Sun, G. 2018. Squeeze-and-excitation networks. CVPR.
- Huang, G.; Liu, Z.; Weinberger, K. Q.; and van der Maaten, L. 2017. Densely connected convolutional networks. In CVPR.
- Klein, A.; Falkner, S.; Springenberg, J. T.; and Hutter, F. 2017. Learning curve prediction with bayesian neural net- works. ICLR.
- Krizhevsky, A., and Hinton, G. 2009. Learning multiple layers of features from tiny images. Master's thesis, Dept. of Computer Science, U. of Toronto.
- Liu, C.; Zoph, B.; Shlens, J.; Hua, W.; Li, L.-J.; Fei-Fei, L.; Yuille, A.; Huang, J.; and Murphy, K. 2018a. Progressive neural architecture search. ECCV.
- Liu, H.; Simonyan, K.; Vinyals, O.; Fernando, C.; and Kavukcuoglu, K. 2018b. Hierarchical representations for efficient architecture search. In ICLR.
- Mendoza, H.; Klein, A.; Feurer, M.; Springenberg, J. T.; and Hutter, F. 2016. Towards automatically-tuned neural net- works. In Workshop on Automatic Machine Learning.
- Miikkulainen, R.; Liang, J.; Meyerson, E.; Rawal, A.; Fink, D.; Francon, O.; Raju, B.; Navruzyan, A.; Duffy, N.; and Hodjat, B. 2017. Evolving deep neural networks. arXiv.
- Negrinho, R., and Gordon, G. 2017. Deeparchitect: Auto- matically designing and training deep architectures. arXiv.
- Pham, H.; Guan, M. Y.; Zoph, B.; Le, Q. V.; and Dean, J. 2018. Faster discovery of neural architectures by searching for paths in a large model. ICLR Workshop.
- Real, E.; Moore, S.; Selle, A.; Saxena, S.; Suematsu, Y. L.; Le, Q.; and Kurakin, A. 2017. Large-scale evolution of image classifiers. In ICML.
- Salimans, T.; Ho, J.; Chen, X.; and Sutskever, I. 2017. Evo- lution strategies as a scalable alternative to reinforcement learning. arXiv.
- Saxena, S., and Verbeek, J. 2016. Convolutional neural fab- rics. In NIPS.
- Srivastava, N.; Hinton, G.; Krizhevsky, A.; Sutskever, I.; and Salakhutdinov, R. 2014. Dropout: A simple way to prevent neural networks from overfitting. JMLR.
- Szegedy, C.; Liu, W.; Jia, Y.; Sermanet, P.; Reed, S.; Anguelov, D.; Erhan, D.; Vanhoucke, V.; and Rabinovich, A. 2015. Going deeper with convolutions. In CVPR.
- Wan, L.; Zeiler, M.; Zhang, S.; Le Cun, Y.; and Fergus, R. 2013. Regularization of neural networks using dropconnect. In ICML.
- Xie, L., and Yuille, A. 2017. Genetic CNN. In ICCV.
- Xie, S.; Girshick, R.; Dollár, P.; Tu, Z.; and He, K. 2017. Ag- gregated residual transformations for deep neural networks. In CVPR.
- Yao, X. 1999. Evolving artificial neural networks. IEEE.
- Zhang, X.; Li, Z.; Loy, C. C.; and Lin, D. 2017. Polynet: A pursuit of structural diversity in very deep networks. In CVPR.
- Zhong, Z.; Yan, J.; and Liu, C.-L. 2018. Practical network blocks design with q-learning. In AAAI.
- Zoph, B., and Le, Q. V. 2016. Neural architecture search with reinforcement learning. In ICLR.
- Zoph, B.; Vasudevan, V.; Shlens, J.; and Le, Q. V. 2018. Learning transferable architectures for scalable im- age recognition. In CVPR.
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Shamsolmoali, P., Chanussot, J., Zareapoor, M., Zhou, H., & Yang, J. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3106442 · Cited by 44 · see citing works
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Hao, C., & Chen, D. (2018) · 2018 14th IEEE International Conference on Solid-State and Integrated Circuit Technology (ICSICT) · doi:10.1109/icsict.2018.8564956 · Cited by 19 · see citing works
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- Authors
- Real, E., Aggarwal, A., Huang, Y., & Le, Q. V.
- Affiliations
- Google (United States) (US)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2019-07-17
- Volume
- 33
- Issue
- 01
- Pages
- 4780-4789
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2785430118
- Cited by
- 328
- References
- 7
- Citations by year
- 2026: 3 · 2025: 21 · 2024: 31 · 2023: 36 · 2022: 46 · 2021: 50 · 2020: 65 · 2019: 59 · 2018: 15 · 2017: 2
- FWCI
- 21.04
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- www.aaai.org/ (unspecified)
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- TIB Data Manager
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Evolutionary Algorithms and Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- neural architecture search, image classification, tournament selection, age-based selection, evolutionary algorithms, ImageNet, reinforcement learning comparison, computational efficiency
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