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Binary Neural Networks (BNNs) show great promise for real-world embedded devices.
LeCun, Y., Denker, J.S., Solla, S.A.: Optimal brain damage. In: Proc. of NeurIPS. pp. 598–605 (1990)
1990
Earlier work this paper cites.
Petersen, K., Pedersen, M., et al.: The matrix cookbook. Technical University of Denmark 15
2008
Earlier work this paper cites.
Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. International Journal of Computer Vision 88
2010
Earlier work this paper cites.
Del Bue, A., Xavier, J., Agapito, L., Paladini, M.: Bilinear modeling via augmented lagrange multipliers (balm). IEEE transactions on pattern analysis and machine intelligence 34
2011
Earlier work this paper cites.
Denil, M., Shakibi, B., Dinh, L., Ranzato, M., De Freitas, N.: Predicting parameters in deep learning. In: Proc. of NeurIPS. pp. 2148–2156 (2013)
2013
Earlier work this paper cites.
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proc. of CVPR. pp. 580–587 (2014)
2014
Earlier work this paper cites.
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: Proc. of ECCV (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: Proc. of NeurIPS. pp. 3123–3131 (2015)
2015
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proc. of ICCV. pp. 1026–1034 (2015)
2015
Earlier work this paper cites.
Lin, T.Y., RoyChowdhury, A., Maji, S.: Bilinear cnn models for fine-grained visual recognition. In: Proc. of ICCV. pp. 1449–1457 (2015)
2015
Earlier work this paper cites.
Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y.: Fitnets: Hints for thin deep nets. In: Proc. of ICLR. pp. 1–13 (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., Berg, A.C., Fei-Fei, L.: Imagenet large scale visual recognition challenge. International Journal of Computer Vision 115
2015
Earlier work this paper cites.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proc. of ICLR. pp. 1–13 (2015)
2015
Earlier work this paper cites.
Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., Bengio, Y.: Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1. In: Proc. of NeurIPS. pp. 1–9 (2016)
2016
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proc. of CVPR. pp. 770–778 (2016)
2016
Earlier work this paper cites.
Li, H., Kadav, A., Durdanovic, I., Samet, H., Graf, H.P.: Pruning filters for efficient convnets. In: Proc. of ICLR. pp. 1–13 (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: Proc. of ECCV. pp. 21–37 (2016)
2016
Cited alongside, same era.
Rastegari, M., Ordonez, V., Redmon, J., Farhadi, A.: Xnor-net: Imagenet classification using binary convolutional neural networks. In: Proc. of ECCV. pp. 525–542 (2016)
2016
Cited alongside, same era.
Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 39
2016
Cited alongside, same era.
2016
Cited alongside, same era.
Lin, S., Ji, R., Yan, C., Zhang, B., Cao, L., Ye, Q., Huang, F., Doermann, D.: Towards optimal structured cnn pruning via generative adversarial learning. In: Proc. of CVPR. pp. 2790–2799 (2019)
2019
Later among the works it cites.
Liu, C., Ding, W., Xia, X., Hu, Y., Zhang, B., Liu, J., Zhuang, B., Guo, G.: Rbcn: Rectified binary convolutional networks for enhancing the performance of 1-bit dcnns. In: Proc. of IJCAI. pp. 854–860 (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: Proc. of CVPR. pp. 2691–2699 (2019)
2019
Later among the works it cites.
Liu, H., Simonyan, K., Yang, Y.: Darts: Differentiable architecture search. In: Proc. of ICLR (2019)
2019
Later among the works it cites.
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2017
Cited alongside, same era.
Lin, S., Ji, R., Chen, C., Huang, F.: Espace: Accelerating convolutional neural networks via eliminating spatial and channel redundancy. In: Proc. of AAAI. pp. 1424–1430 (2017)
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: Proc. of ICCV. pp. 2736–2744 (2017)
2017
Cited alongside, same era.
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., Lerer, A.: Automatic differentiation in pytorch. In: NeurIPS Workshops (2017)
2017
Cited alongside, same era.
Yu, Z., Yu, J., Fan, J., Tao, D.: Multi-modal factorized bilinear pooling with co-attention learning for visual question answering. In: Proc. of ICCV. pp. 1821–1830 (2017)
2017
Cited alongside, same era.
He, Y., Kang, G., Dong, X., Fu, Y., Yang, Y.: Soft filter pruning for accelerating deep convolutional neural networks. In: Proc. of IJCAI. pp. 2234–2240 (2018)
2018
Cited alongside, same era.
Huang, Z., Wang, N.: Data-driven sparse structure selection for deep neural networks. In: Proc. of ECCV. pp. 304–320 (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: Proc. of ECCV (2018)
2018
Cited alongside, same era.
Lin, M., Ji, R., Xu, Z., Zhang, B., Wang, Y., Wu, Y., Huang, F., Lin, C.W.: Rotated binary neural network. In: Proc. of NeurIPS. pp. 1–9 (2020)
2020
Later among the works it cites.
Liu, Z., Shen, Z., Savvides, M., Cheng, K.T.: Reactnet: Towards precise binary neural network with generalized activation functions. In: Proc. of ECCV. pp. 143–159 (2020)
2020
Later among the works it cites.
Qin, H., Gong, R., Liu, X., Shen, M., Wei, Z., Yu, F., Song, J.: Forward and backward information retention for accurate binary neural networks. In: Proc. of CVPR. pp. 2250–2259 (2020)
2020
Later among the works it cites.
Wang, Z., Wu, Z., Lu, J., Zhou, J.: Bidet: An efficient binarized object detector. In: Proc. of CVPR. pp. 2049–2058 (2020)
2020
Later among the works it cites.
Yang, Z., Wang, Y., Han, K., Xu, C., Xu, C., Tao, D., Xu, C.: Searching for low-bit weights in quantized neural networks. In: Proc. of NeurIPS. pp. 1–11 (2020)
2020
Later among the works it cites.
Feng, J.: Bolt. https://github.com/huawei-noah/bolt (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
Xu, S., Li, Y., Zhao, J., Zhang, B., Guo, G.: Poem: 1-bit point-wise operations based on expectation-maximization for efficient point cloud processing. In: Proc. of BMVC. pp. 1–10 (2021)
2021
Later among the works it cites.
Xu, S., Zhao, J., Lu, J., Zhang, B., Han, S., Doermann, D.: Layer-wise searching for 1-bit detectors. In: Proc. of CVPR. pp. 5682–5691 (2021)
2021
Later among the works it cites.
Xu, Z., Lin, M., Liu, J., Chen, J., Shao, L., Gao, Y., Tian, Y., Ji, R.: Recu: Reviving the dead weights in binary neural networks. In: Proc. of ICCV. pp. 5198–5208 (2021)
2021
Later among the works it cites.
2022
Closest in time.
Zhao, J., Xu, S., Zhang, B., Gu, J., Doermann, D., Guo, G.: Towards compact 1-bit cnns via bayesian learning. International Journal of Computer Vision 130
2022
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