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This paper proposes Binary ArchitecTure Search (BATS), a framework that drastically reduces the accuracy gap between binary neural networks and their real-valued counterparts by means of Neural Architecture Search (NAS).
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: A large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition. pp. 248–255 (2009)
2009
Earlier work this paper cites.
Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images. Tech. rep. (2009)
2009
Earlier work this paper cites.
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 on Neural Information Processing Systems (2015)
2015
Earlier work this paper cites.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition (2015)
2015
Earlier work this paper cites.
2016
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.
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Rastegari, M., Ordonez, V., Redmon, J., Farhadi, A.: XNOR-Net: ImageNet classification using binary convolutional neural networks. In: European Conference on Computer Vision. pp. 525–542 (2016)
2016
Earlier work this paper cites.
Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016)
2016
Earlier work this paper cites.
Zhou, S., Wu, Y., Ni, Z., Zhou, X., Wen, H., Zou, Y.: DoReFa-Net: Training low bitwidth convolutional neural networks with low bitwidth gradients. arXiv (2016)
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Baker, B., Gupta, O., Naik, N., Raskar, R.: Designing neural network architectures using reinforcement learning. In: International Conference on Learning Representations (2017)
2017
Earlier work this paper cites.
Bulat, A., Tzimiropoulos, G.: Binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources. In: IEEE International Conference on Computer Vision. pp. 3706–3714 (2017)
2017
Earlier work this paper cites.
Cai, Z., He, X., Sun, J., Vasconcelos, N.: Deep learning with low precision by half-wave gaussian quantization. In: IEEE Conference on Computer Vision and Pattern Recognition. pp. 5918–5926 (2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Lin, X., Zhao, C., Pan, W.: Towards accurate binary convolutional neural network. In: Advances on Neural Information Processing Systems. pp. 345–353 (2017)
2017
Cited alongside, same era.
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: International Conference on Machine Learning. pp. 2902–2911 (2017)
2017
Cited alongside, same era.
Xie, L., Yuille, A.: Genetic CNN. In: IEEE International Conference on Computer Vision. pp. 1379–1388 (2017)
2017
Cited alongside, same era.
Brock, A., Lim, T., Ritchie, J., Weston, N.: SMASH: One-shot model architecture search through hypernetworks. In: International Conference on Learning Representations (2018)
2018
Cited alongside, same era.
Faraone, J., Fraser, N., Blott, M., Leong, P.H.: Syq: Learning symmetric quantization for efficient deep neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition. pp. 4300–4309 (2018)
Bulat, A., Tzimiropoulos, G.: Xnor-net++: Improved binary neural networks. In: British Machine Vision Conference (2019)
2019
Later among the works it cites.
Cai, H., Zhu, L., Han, S.: ProxylessNAS: Direct neural architecture search on target task and hardware. International Conference on Learning Representations (2019)
2019
Later among the works it cites.
Chen, X., Xie, L., Wu, J., Tian, Q.: Progressive differentiable architecture search: Bridging the depth gap between search and evaluation. IEEE International Conference on Computer Vision (2019)
2019
Later among the works it cites.
Cubuk, E.D., Zoph, B., Mane, D., Vasudevan, V., Le, Q.V.: AutoAugment: Learning augmentation policies from data. IEEE Conference on Computer Vision and Pattern Recognition (2019)
2019
Later among the works it cites.
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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.
Liu, C., Zoph, B., Neumann, M., Shlens, J., Hua, W., Li, L.J., Fei-Fei, L., Yuille, A., Huang, J., Murphy, K.: Progressive neural architecture search. In: European Conference on Computer Vision. pp. 19–34 (2018)
2018
Cited alongside, same era.
Liu, Z., Wu, B., Luo, W., Yang, X., Liu, W., Cheng, K.T.: Bi-Real Net: Enhancing the performance of 1-bit CNNs with improved representational capability and advanced training algorithm. In: European Conference on Computer Vision. pp. 747–763 (2018)
2018
Cited alongside, same era.
Ma, N., Zhang, X., Zheng, H.T., Sun, J.: Shufflenet v2: Practical guidelines for efficient cnn architecture design. In: European Conference on Computer Vision. pp. 122–138 (2018)
2018
Cited alongside, same era.
Pham, H., Guan, M., Zoph, B., Le, Q., Dean, J.: Efficient neural architecture search via parameters sharing. In: International Conference on Machine Learning. pp. 4095–4104 (2018)
2018
Cited alongside, same era.
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: Inverted residuals and linear bottlenecks. In: IEEE Conference on Computer Vision and Pattern Recognition. pp. 4510–4520 (2018)
2018
Cited alongside, same era.
Xie, S., Zheng, H., Liu, C., Lin, L.: Snas: stochastic neural architecture search. In: International Conference on Learning Representations (2018)
2018
Cited alongside, same era.
2019
Later among the works it cites.
Helwegen, K., Widdicombe, J., Geiger, L., Liu, Z., Cheng, K.T., Nusselder, R.: Latent weights do not exist: Rethinking binarized neural network optimization. In: Advances in neural information processing systems. pp. 7533–7544 (2019)
2019
Later among the works it cites.
Liu, C., Chen, L.C., Schroff, F., Adam, H., Hua, W., Yuille, A.L., Fei-Fei, L.: Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition. pp. 82–92 (2019)
2019
Later among the works it cites.
Liu, C., Ding, W., Xia, X., Zhang, B., Gu, J., Liu, J., Ji, R., Doermann, D.: Circulant binary convolutional networks: Enhancing the performance of 1-bit dcnns with circulant back propagation. In: IEEE Conference on Computer Vision and Pattern Recognition. pp. 2691–2699 (2019)
2019
Later among the works it cites.
Liu, H., Simonyan, K., Yang, Y.: DARTS: Differentiable architecture search. International Conference on Learning Representations (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., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, pp. 8024–8035 (2019)
2019
Later among the works it cites.
Real, E., Aggarwal, A., Huang, Y., Le, Q.V.: Regularized evolution for image classifier architecture search. In: AAAI Conf. on Artificial Intelligence. vol. 33, pp. 4780–4789 (2019)
2019
Later among the works it cites.
Shen, M., Han, K., Xu, C., Wang, Y.: Searching for accurate binary neural architectures. In: Proceedings of the IEEE International Conference on Computer Vision Workshops. pp. 0–0 (2019)
2019
Later among the works it cites.
Wang, Z., Lu, J., Tao, C., Zhou, J., Tian, Q.: Learning channel-wise interactions for binary convolutional neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition. pp. 568–577 (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
Zhu, S., Dong, X., Su, H.: Binary ensemble neural network: More bits per network or more networks per bit? In: IEEE Conference on Computer Vision and Pattern Recognition. pp. 4923–4932 (2019)
2019
Later among the works it cites.
Zhuang, B., Shen, C., Tan, M., Liu, L., Reid, I.: Structured binary neural networks for accurate image classification and semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition. pp. 413–422 (2019)
2019
Later among the works it cites.