Particle Swarm Optimisation for Evolving Deep Neural Networks for Image Classification by Evolving and Stacking Transferable Blocks

R. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” in MHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science . Ieee, 1995, pp. 39–43

1995

Earlier work this paper cites.

F. Van den Bergh and A. P. Engelbrecht, “A study of particle swarm optimization particle trajectories,” Information sciences , vol. 176, no. 8, pp. 937–971, 2006

2006

Earlier work this paper cites.

A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images,” Citeseer, Tech. Rep., 2009

2009

Earlier work this paper cites.

J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE conference on computer vision and pattern recognition . Ieee, 2009, pp. 248–255

2009

Earlier work this paper cites.

V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in Proceedings of the 27th international conference on machine learning (ICML-10) , 2010, pp. 807–814

2010

Earlier work this paper cites.

Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng, “Reading digits in natural images with unsupervised feature learning,” 2011

2011

Earlier work this paper cites.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems , 2012, pp. 1097–1105

2012

Earlier work this paper cites.

I. Sutskever, J. Martens, G. E. Dahl, and G. E. Hinton, “On the importance of initialization and momentum in deep learning,” ICML (3) , vol. 28, no. 1139-1147, p. 5, 2013

2013

Earlier work this paper cites.

M. Lin, Q. Chen, and S. Yan, “Network in network,” arXiv preprint arXiv:1312.4400 , 2013

Original

2013

Earlier work this paper cites.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556 , 2014

Original

2014

Earlier work this paper cites.

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , 2014

Original

2014

Earlier work this paper cites.

S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” arXiv preprint arXiv:1502.03167 , 2015

Original

2015

Earlier work this paper cites.

C.-Y. Lee, S. Xie, P. Gallagher, Z. Zhang, and Z. Tu, “Deeply-supervised nets,” in Artificial intelligence and statistics , 2015, pp. 562–570

2015

Cited alongside, same era.

K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 1026–1034

2015

Cited alongside, same era.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778

2016

Cited alongside, same era.

B. Zoph and Q. V. Le, “Neural architecture search with reinforcement learning,” arXiv preprint arXiv:1611.01578 , 2016

Original

2016

Cited alongside, same era.

G. Larsson, M. Maire, and G. Shakhnarovich, “Fractalnet: Ultra-deep neural networks without residuals,” arXiv preprint arXiv:1605.07648 , 2016

H. Liu, K. Simonyan, O. Vinyals, C. Fernando, and K. Kavukcuoglu, “Hierarchical representations for efficient architecture search,” arXiv preprint arXiv:1711.00436 , 2017

Original

2017

Later among the works it cites.

E. Real, S. Moore, A. Selle, S. Saxena, Y. L. Suematsu, J. Tan, Q. V. Le, and A. Kurakin, “Large-scale evolution of image classifiers,” in Proceedings of the 34th International Conference on Machine Learning-Volume 70 . JMLR. org, 2017, pp. 2902–2911

2017

Later among the works it cites.

B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le, “Learning transferable architectures for scalable image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 8697–8710

2018

Later among the works it cites.

B. Wang, Y. Sun, B. Xue, and M. Zhang, “Evolving deep convolutional neural networks by variable-length particle swarm optimization for image classification,” in 2018 IEEE Congress on Evolutionary Computation (CEC) . IEEE, 2018, pp. 1–8

2018