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Quantized deep neural networks (QDNNs) are necessary for low-power, high throughput, and embedded applications.
Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Tech. rep., Citeseer (2009)
2009
Earlier work this paper cites.
Hariharan, B., Arbeláez, P., Bourdev, L., Maji, S., Malik, J.: Semantic contours from inverse detectors. In: 2011 International Conference on Computer Vision. pp. 991–998. IEEE (2011)
2011
Earlier work this paper cites.
2013
Earlier work this paper cites.
Hwang, K., Sung, W.: Fixed-point feedforward deep neural network design using weights +1, 0, and -1. In: Signal Processing Systems (SiPS), 2014 IEEE Workshop on. pp. 1–6. IEEE (2014)
2014
Earlier work this paper cites.
Courbariaux, M., Bengio, Y., David, J.P.: Binaryconnect: Training deep neural networks with binary weights during propagations. In: Advances in Neural Information Processing Systems (NIPS). pp. 3123–3131 (2015)
2015
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.
Gupta, S., Agrawal, A., Gopalakrishnan, K., Narayanan, P.: Deep learning with limited numerical precision. In: International Conference on Machine Learning (ICML). pp. 1737–1746 (2015)
2015
Earlier work this paper cites.
Lee, C.Y., Xie, S., Gallagher, P., Zhang, Z., Tu, Z.: Deeply-supervised nets. In: Artificial Intelligence and Statistics. pp. 562–570 (2015)
2015
Earlier work this paper cites.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International Journal of Computer Vision 115
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
Andri, R., Cavigelli, L., Rossi, D., Benini, L.: Yodann: An ultra-low power convolutional neural network accelerator based on binary weights. In: 2016 IEEE Computer Society Annual Symposium on VLSI (ISVLSI). pp. 236–241. IEEE (2016)
2016
Earlier work this paper cites.
Fengfu, L., Bo, Z., Bin, L.: Ternary weight networks. In: NIPS Workshop on EMDNN. vol. 118, p. 119 (2016)
2016
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 770–778. IEEE (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.
Lin, D., Talathi, S., Annapureddy, S.: Fixed point quantization of deep convolutional networks. In: International Conference on Machine Learning. pp. 2849–2858 (2016)
2016
Earlier work this paper cites.
Rastegari, M., Ordonez, V., Redmon, J., Farhadi, A.: Xnor-net: Imagenet classification using binary convolutional neural networks. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 525–542. Springer (2016)
2016
Cited alongside, same era.
2016
Cited alongside, same era.
Zagoruyko, S., Komodakis, N.: Wide residual networks. arXiv preprint arXiv:1605.07146 (2016)
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2018
Later among the works it cites.
Galloway, A., Taylor, G.W., Moussa, M.: Attacking binarized neural networks. In: International Conference on Learning Representations (ICLR) (2018)
2018
Later among the works it cites.
Hou, L., Kwok, J.T.: Loss-aware weight quantization of deep networks. International Conference on Learning Representations (ICLR) (2018)
2018
Later among the works it cites.
Kurakin, A., Goodfellow, I., Bengio, S., Dong, Y., Liao, F., Liang, M., Pang, T., Zhu, J., Hu, X., Xie, C., et al.: Adversarial attacks and defences competition. In: The NIPS’17 Competition: Building Intelligent Systems, pp. 195–231. Springer (2018)
2018
Later among the works it cites.
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Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence 40
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Cisse, M., Bojanowski, P., Grave, E., Dauphin, Y., Usunier, N.: Parseval networks: Improving robustness to adversarial examples. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70. pp. 854–863. JMLR. org (2017)
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Park, E., Ahn, J., Yoo, S.: Weighted-entropy-based quantization for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 5456–5464 (2017)
2017
Cited alongside, same era.
Smith, L.N.: Cyclical learning rates for training neural networks. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV). pp. 464–472. IEEE (2017)
2017
Cited alongside, same era.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Advances in neural information processing systems. pp. 5998–6008 (2017)
2017
Cited alongside, same era.
Liao, F., Liang, M., Dong, Y., Pang, T., Hu, X., Zhu, J.: Defense against adversarial attacks using high-level representation guided denoiser. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1778–1787 (2018)
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
Sakr, C., Shanbhag, N.: Minimum precision requirements for deep learning with biomedical datasets. In: 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS). pp. 1–4. IEEE (2018)
2018
Later among the works it cites.
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4510–4520 (2018)
2018
Later among the works it cites.
Zhang, D., Yang, J., Ye, D., Hua, G.: LQ-Nets: Learned quantization for highly accurate and compact deep neural networks. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 365–382 (2018)
2018
Later among the works it cites.
Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V.: Learning transferable architectures for scalable image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 8697–8710 (2018)
2018
Later among the works it cites.
Boo, Y., Shin, S., Sung, W.: Memorization capacity of deep neural networks under parameter quantization. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 1383–1387. IEEE (2019)
2019
Later among the works it cites.
Gong, R., Liu, X., Jiang, S., Li, T., Hu, P., Lin, J., Yu, F., Yan, J.: Differentiable soft quantization: Bridging full-precision and low-bit neural networks. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 4852–4861 (2019)
2019
Later among the works it cites.
Jung, S., Son, C., Lee, S., Son, J., Han, J.J., Kwak, Y., Hwang, S.J., Choi, C.: Learning to quantize deep networks by optimizing quantization intervals with task loss. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4350–4359 (2019)
2019
Later among the works it cites.
Lin, J., Gan, C., Han, S.: Defensive quantization: When efficiency meets robustness. International Conference on Learning Representations (ICLR) (2019)
2019
Later among the works it cites.