Rethinking Bottleneck Structure for Efficient Mobile Network Design

Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results. http://www.pascal-network.org/challenges/VOC/voc2012/workshop/index.html

2012

Earlier work this paper cites.

Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems. pp. 1097–1105 (2012)

2012

Earlier work this paper cites.

Jaderberg, M., Vedaldi, A., Zisserman, A.: Speeding up convolutional neural networks with low rank expansions. arXiv preprint arXiv:1405.3866 (2014)

Original

2014

Earlier work this paper cites.

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

Original

2014

Earlier work this paper cites.

Everingham, M., Eslami, S.A., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes challenge: A retrospective. International journal of computer vision 111

2015

Earlier work this paper cites.

Han, S., Mao, H., Dally, W.J.: Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149 (2015)

Original

2015

Earlier work this paper cites.

Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)

Original

2015

Earlier work this paper cites.

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

2016

Earlier work this paper cites.

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)

2016

Earlier work this paper cites.

He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: European conference on computer vision. pp. 630–645. Springer (2016)

2016

Earlier work this paper cites.

Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: Ssd: Single shot multibox detector. In: European conference on computer vision. pp. 21–37. Springer (2016)

2016

Earlier work this paper cites.

Zagoruyko, S., Komodakis, N.: Wide residual networks. arXiv preprint arXiv:1605.07146 (2016)

Original

2016

Earlier work this paper cites.

Chen, Y., Li, J., Xiao, H., Jin, X., Yan, S., Feng, J.: Dual path networks. In: Advances in neural information processing systems. pp. 4467–4475 (2017)

2017

Earlier work this paper cites.

Chollet, F.: Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1251–1258 (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.

Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R., Bengio, Y.: Quantized neural networks: Training neural networks with low precision weights and activations. The Journal of Machine Learning Research 18

2017

Cited alongside, same era.

Liu, Z., Li, J., Shen, Z., Huang, G., Yan, S., Zhang, C.: Learning efficient convolutional networks through network slimming. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 2736–2744 (2017)

2017

Cited alongside, same era.

Migacz, S.: Nvidia 8-bit inference width tensorrt. In: GPU Technology Conference (2017)

2017

Cited alongside, same era.

Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1492–1500 (2017)

2017

Cited alongside, same era.

Li, D., Zhou, A., Yao, A.: Hbonet: Harmonious bottleneck on two orthogonal dimensions. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 3316–3325 (2019)

2019

Later among the works it cites.

Li, X., Wang, W., Hu, X., Yang, J.: Selective kernel networks. In: CVPR. pp. 510–519 (2019)

2019

Later among the works it cites.

Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems. pp. 8024–8035 (2019)

2019

Later among the works it cites.

Radu, V., Kaszyk, K., Wen, Y., Turner, J., Cano, J., Crowley, E.J., Franke, B., Storkey, A., O’Boyle, M.: Performance aware convolutional neural network channel pruning for embedded gpus (2019)

2019

Later among the works it cites.