Inception-v4, inception-ResNet and the impact of residual connections on learning

31st AAAI Conference on Artificial Intelligence, AAAI 2017

Volumn , Issue , 2017, Pages 4278-4284

  Szegedy, Christian ^a   Ioffe, Sergey ^a   Vanhoucke, Vincent ^a   Alemi, Alexander A ^a  

^a GOOGLE INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); IMAGE RECOGNITION;

CLASSIFICATION TASKS; COMPUTATIONAL COSTS; CONVOLUTIONAL NETWORKS; SINGLE FRAMES; STATE-OF-THE-ART PERFORMANCE; TEST SETS; TRADITIONAL ARCHITECTURE;

NETWORK ARCHITECTURE;

EID: 85028013193     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. Abadi, M.; Agarwal, A.; Barham, P.; Brevdo, E.; Chen, Z.; Citro, C.; Corrado, G. S.; Davis, A.; Dean, J.; Devin, M.; Ghemawat, S.; Goodfellow, I.; Harp, A.; Irving, G.; Isard, M.; Jia, Y.; Jozefowicz, R.; Kaiser, L.; Kudlur, M.; Levenberg, J.; Mané, D.; Monga, R.; Moore, S.; Murray, D.; Olah, C.; Schuster, M.; Shlens, J.; Steiner, B.; Sutskever, I.; Talwar, K.; Tucker, P.; Vanhoucke, V.; Vasudevan, V.; Viégas, F.; Vinyals, O.; Warden, P.; Wattenberg, M.; Wicke, M.; Yu, Y.; and Zheng, X. 2015. TensorFlow: Large-scale machine learning on heterogeneous systems. Software available from tensorflow. org.
2. Dean, J.; Corrado, G.; Monga, R.; Chen, K.; Devin, M.; Mao, M.; Senior, A.; Tucker, P.; Yang, K.; Le, Q. V.; et al. 2012. Large scale distributed deep networks. In Advances in Neural Information Processing Systems, 1223-1231.
3. Dong, C.; Loy, C. C.; He, K.; and Tang, X. 2014. Learning a deep convolutional network for image super-resolution. In Computer Vision-ECCV 2014. Springer. 184-199.
4. Fukushima, K. 1980. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological cybernetics 36 (4): 193-202.
5. Girshick, R.; Donahue, J.; Darrell, T.; and Malik, J. 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
6. He, K.; Zhang, X.; Ren, S.; and Sun, J. 2015. Deep residual learning for image recognition. arXiv preprint arXiv: 1512. 03385.
7. Ioffe, S., and Szegedy, C. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Proceedings of The 32nd International Conference on Machine Learning, 448-456.
8. Karpathy, A.; Toderici, G.; Shetty, S.; Leung, T.; Sukthankar, R.; and Fei-Fei, L. 2014. Large-scale video classification with convolutional neural networks. In Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, 1725-1732. IEEE.
9. Krizhevsky, A.; Sutskever, I.; and Hinton, G. E. 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, 1097-1105.
10. LeCun, Y.; Boser, B.; Denker, J. S.; Henderson, D.; Howard, R. E.; Hubbard, W.; and Jackel, L. D. 1989. Backpropagation applied to handwritten zip code recognition. Neural computation 1 (4): 541-551.
11. Lin, M.; Chen, Q.; and Yan, S. 2013. Network in network. arXiv preprint arXiv: 1312. 4400.
12. Long, J.; Shelhamer, E.; and Darrell, T. 2015. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3431-3440.
13. Pascanu, R.; Mikolov, T.; and Bengio, Y. 2012. On the difficulty of training recurrent neural networks. arXiv preprint arXiv: 1211. 5063.
14. Russakovsky, O.; Deng, J.; Su, H.; Krause, J.; Satheesh, S.; Ma, S.; Huang, Z.; Karpathy, A.; Khosla, A.; Bernstein, M.; et al. 2014. Imagenet large scale visual recognition challenge.
15. Simonyan, K., and Zisserman, A. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv: 1409. 1556.
16. Sutskever, I.; Martens, J.; Dahl, G.; and Hinton, G. 2013. On the importance of initialization and momentum in deep learning. In Proceedings of the 30th International Conference on Machine Learning (ICML-13), volume 28, 1139-1147. JMLR Workshop and Conference Proceedings.
17. Szegedy, C.; Liu, W.; Jia, Y.; Sermanet, P.; Reed, S.; Anguelov, D.; Erhan, D.; Vanhoucke, V.; and Rabinovich, A. 2015a. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1-9.
18. Szegedy, C.; Vanhoucke, V.; Ioffe, S.; Shlens, J.; andWojna, Z. 2015b. Rethinking the inception architecture for computer vision. arXiv preprint arXiv: 1512. 00567.
19. Tieleman, T., and Hinton, G. Divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks for Machine Learning, 4, 2012. Accessed: 2015-11-05.
20. Toshev, A., and Szegedy, C. 2014. Deeppose: Human pose estimation via deep neural networks. In Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, 1653-1660. IEEE.
21. Wang, N., and Yeung, D.-Y. 2013. Learning a deep compact image representation for visual tracking. In Advances in Neural Information Processing Systems, 809-817.