MeliusNet: Can Binary Neural Networks Achieve MobileNet-level Accuracy?

Liu, L., Jiang, H., He, P., Chen, W., Liu, X., Gao, J., Han, J.: On the Variance of the Adaptive Learning Rate and Beyond. arXiv preprint arXiv:1908.03265 (2019), http://arxiv.org/abs/1908.03265

Original

1908

Earlier work this paper cites.

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. Ieee (2009)

2009

Earlier work this paper cites.

Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics. pp. 249–256 (2010)

2010

Earlier work this paper cites.

Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems. pp. 1097–1105 (2012)

2012

Earlier work this paper cites.

Bengio, Y., Léonard, N., Courville, A.C.: Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation. CoRR abs/1308.3

Original

2013

Earlier work this paper cites.

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in neural information processing systems. pp. 2672–2680 (2014)

2014

Earlier work this paper cites.

Jaderberg, M., Vedaldi, A., Zisserman, A.: Deep Features for Text Spotting. In: Computer Vision – ECCV 2014. pp. 512–528. Springer International Publishing, Cham (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. pp. 3123–3131 (2015)

2015

Earlier work this paper cites.

Han, S., Pool, J., Tran, J., Dally, W.: Learning both Weights and Connections for Efficient Neural Networks. In: Advances in Neural Information Processing Systems. pp. 1135–1143 (2015)

2015

Earlier work this paper cites.

Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: ICLR (2015)

2015

Earlier work this paper cites.

Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. In: Advances in Neural Information Processing Systems 28. pp. 91–99 (2015)

2015

Earlier work this paper cites.

Han, S., Mao, H., Dally, W.J.: Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding. In: International Conference on Learning Representations (ICLR) (2016)

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.

Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R., Bengio, Y.: Binarized neural networks. In: Advances in neural information processing systems. pp. 4107–4115 (2016)

2016

Earlier work this paper cites.

Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: Squeezenet: Alexnet-level accuracy with 50x fewer parameters and 0.5 mb model size. arXiv preprint arXiv:1602.07360 (2016)

Original

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. Springer (2016)

2016

Earlier work this paper cites.

Redmon, J., Divvala, S.K., Girshick, R.B., Farhadi, A.: You Only Look Once: Unified, Real-Time Object Detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 779–788 (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 preprint arXiv:1606.06160 (2016), http://arxiv.org/abs/1606.06160

Original

2016

Cited alongside, same era.

Arjovsky, M., Bottou, L.: Towards Principled Methods for Training Generative Adversarial Networks. International Conference on Learning Representations (ICLR) (2017)

2017

Cited alongside, same era.

Cai, Z., He, X., Sun, J., Vasconcelos, N.: Deep learning with low precision by half-wave gaussian quantization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 5918–5926 (2017)

2017

Cited alongside, same era.

Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (2017), http://arxiv.org/abs/1704.04861

Original

2017

Cited alongside, same era.

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.

Zhang, X., Zhou, X., Lin, M., Sun, J.: ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)

2018

Later among the works it cites.

Alizadeh, M., Fernández-Marqués, J., Lane, N.D., Gal, Y.: An Empirical study of Binary Neural Networks’ Optimisation. International Conference on Learning Representations (2019)

2019

Later among the works it cites.

Bethge, J., Yang, H., Bornstein, M., Meinel, C.: BinaryDenseNet: Developing an Architecture for Binary Neural Networks. In: The IEEE International Conference on Computer Vision (ICCV) Workshops (2019)

2019

Later among the works it cites.