Rethinking the Inception Architecture for Computer Vision

Articles citing Rethinking the Inception Architecture for Computer Vision

Title / Author	Year
Deep learning I Goodfellow, Y Bengio, A Courville MIT press	2016
Attention is all you need A Vaswani, N Shazeer, N Parmar, J Uszkoreit, L Jones, AN Gomez, ... Advances in neural information processing systems, 5998-6008	2017
Densely connected convolutional networks G Huang, Z Liu, L Van Der Maaten, KQ Weinberger Proceedings of the IEEE conference on computer vision and pattern …	2017
Ssd: Single shot multibox detector W Liu, D Anguelov, D Erhan, C Szegedy, S Reed, CY Fu, AC Berg European conference on computer vision, 21-37	2016
Tensorflow: A system for large-scale machine learning M Abadi, P Barham, J Chen, Z Chen, A Davis, J Dean, M Devin, ... 12th {USENIX} symposium on operating systems design and implementation …	2016
Inception-v4, inception-resnet and the impact of residual connections on learning C Szegedy, S Ioffe, V Vanhoucke, AA Alemi Thirty-first AAAI conference on artificial intelligence	2017
Mobilenets: Efficient convolutional neural networks for mobile vision applications AG Howard, M Zhu, B Chen, D Kalenichenko, W Wang, T Weyand, ... arXiv preprint arXiv:1704.04861	2017
Dermatologist-level classification of skin cancer with deep neural networks A Esteva, B Kuprel, RA Novoa, J Ko, SM Swetter, HM Blau, S Thrun nature 542 (7639), 115-118	2017
Identity mappings in deep residual networks K He, X Zhang, S Ren, J Sun European conference on computer vision, 630-645	2016
Improved techniques for training gans T Salimans, I Goodfellow, W Zaremba, V Cheung, A Radford, X Chen Advances in neural information processing systems, 2234-2242	2016
Squeeze-and-excitation networks J Hu, L Shen, G Sun Proceedings of the IEEE conference on computer vision and pattern …	2018
Xception: Deep learning with depthwise separable convolutions F Chollet Proceedings of the IEEE conference on computer vision and pattern …	2017
R-fcn: Object detection via region-based fully convolutional networks J Dai, Y Li, K He, J Sun Advances in neural information processing systems, 379-387	2016
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size FN Iandola, S Han, MW Moskewicz, K Ashraf, WJ Dally, K Keutzer arXiv preprint arXiv:1602.07360	2016
Aggregated residual transformations for deep neural networks S Xie, R Girshick, P Dollár, Z Tu, K He Proceedings of the IEEE conference on computer vision and pattern …	2017
Towards evaluating the robustness of neural networks N Carlini, D Wagner 2017 ieee symposium on security and privacy (sp), 39-57	2017
Grad-cam: Visual explanations from deep networks via gradient-based localization RR Selvaraju, M Cogswell, A Das, R Vedantam, D Parikh, D Batra Proceedings of the IEEE international conference on computer vision, 618-626	2017
Understanding deep learning requires rethinking generalization C Zhang, S Bengio, M Hardt, B Recht, O Vinyals arXiv preprint arXiv:1611.03530	2016
Adversarial examples in the physical world A Kurakin, I Goodfellow, S Bengio arXiv preprint arXiv:1607.02533	2016
3D U-Net: learning dense volumetric segmentation from sparse annotation Ö Çiçek, A Abdulkadir, SS Lienkamp, T Brox, O Ronneberger International conference on medical image computing and computer-assisted …	2016

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