BS-NAS: Broadening-and-Shrinking One-Shot NAS with Searchable Numbers of Channels

Stanley, K.O., Miikkulainen, R.: Evolving neural networks through augmenting topologies. Evolutionary computation 10

2002

Earlier work this paper cites.

Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V.: Learning transferable architectures for scalable image recognition. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 8697–8710. IEEE (2018)

Original

2003

Earlier work this paper cites.

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

2009

Earlier work this paper cites.

Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning. pp. 448–456 (2015)

2015

Earlier work this paper cites.

Baker, B., Gupta, O., Naik, N., Raskar, R.: Designing neural network architectures using reinforcement learning. arXiv preprint arXiv:1611.02167 (2016)

Original

2016

Earlier work this paper cites.

Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 (2016)

Original

2016

Earlier work this paper cites.

Zoph, B., Le, Q.V.: Neural architecture search with reinforcement learning. arXiv preprint arXiv:1611.01578 (2016)

Original

2016

Earlier work this paper cites.

Goyal, P., Dollár, P., Girshick, R., Noordhuis, P., Wesolowski, L., Kyrola, A., Tulloch, A., Jia, Y., He, K.: Accurate, large minibatch sgd: Training imagenet in 1 hour. arXiv preprint arXiv:1706.02677 (2017)

Original

2017

Earlier work this paper cites.

He, Y., Zhang, X., Sun, J.: Channel pruning for accelerating very deep neural networks. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 1389–1397 (2017)

2017

Earlier work this paper cites.

Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)

Original

2017

Earlier work this paper cites.

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

2017

Earlier work this paper cites.

Xie, L., Yuille, A.: Genetic cnn. In: Proceedings of the IEEE international conference on computer vision. pp. 1379–1388 (2017)

2017

Earlier work this paper cites.

Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)

Original

2017

Earlier work this paper cites.

Bender, G., Kindermans, P.J., Zoph, B., Vasudevan, V., Le, Q.: Understanding and simplifying one-shot architecture search. In: International Conference on Machine Learning. pp. 550–559 (2018)

2018

Cited alongside, same era.

Brock, A., Lim, T., Ritchie, J.M., Weston, N.J.: Smash: One-shot model architecture search through hypernetworks. In: 6th International Conference on Learning Representations (2018)

2018

Cited alongside, same era.

He, Y., Lin, J., Liu, Z., Wang, H., Li, L.J., Han, S.: Amc: Automl for model compression and acceleration on mobile devices. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 784–800 (2018)

2018

Cited alongside, same era.

Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 7132–7141 (2018)

2018

Cited alongside, same era.

Chu, X., Zhang, B., Li, J., Li, Q., Xu, R.: Scarletnas: Bridging the gap between scalability and fairness in neural architecture search. arXiv preprint arXiv:1908.06022 (2019)

Original

2019

Later among the works it cites.

Chu, X., Zhang, B., Xu, R., Li, J.: Fairnas: Rethinking evaluation fairness of weight sharing neural architecture search. arXiv preprint arXiv:1907.01845 (2019)

Original

2019

Later among the works it cites.

Cubuk, E.D., Zoph, B., Mane, D., Vasudevan, V., Le, Q.V.: Autoaugment: Learning augmentation strategies from data. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 113–123 (2019)

2019

Later among the works it cites.

Dai, X., Zhang, P., Wu, B., Yin, H., Sun, F., Wang, Y., Dukhan, M., Hu, Y., Wu, Y., Jia, Y., et al.: Chamnet: Towards efficient network design through platform-aware model adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 11398–11407 (2019)

2019