Fetching the paper…
Reading the bibliography…
Neural network compression has recently received much attention due to the computational requirements of modern deep models.
LeCun, Y., Boser, B.E., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W.E., Jackel, L.D.: Handwritten digit recognition with a back-propagation network. In: Advances in neural information processing systems. pp. 396–404 (1990)
1990
Earlier work this paper cites.
Schmidhuber, J.: Learning complex, extended sequences using the principle of history compression. Neural Computation 4
1992
Earlier work this paper cites.
Hassibi, B., Stork, D.G.: Second order derivatives for network pruning: Optimal brain surgeon. In: Advances in neural information processing systems. pp. 164–171 (1993)
1993
Earlier work this paper cites.
Ström, N.: Phoneme probability estimation with dynamic sparsely connected artificial neural networks. The Free Speech Journal 5
1997
Earlier work this paper cites.
Ng, A.Y.: Feature selection, l 1 vs. l 2 regularization, and rotational invariance. In: Proceedings of the twenty-first international conference on Machine learning. p. 78. ACM (2004)
2004
Earlier work this paper cites.
Buciluǎ, C., Caruana, R., Niculescu-Mizil, A.: Model compression. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. pp. 535–541. ACM (2006)
2006
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: Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. pp. 248–255. IEEE (2009)
2009
Earlier work this paper cites.
Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images (2009)
2009
Earlier work this paper cites.
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning. In: NIPS Workshop on Deep Learning and Unsupervised Feature Learning 2011 (2011)
2011
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.
Lin, M., Chen, Q., Yan, S.: Network in network. arXiv preprint arXiv:1312.4400 (2013)
2013
Earlier work this paper cites.
Rigamonti, R., Sironi, A., Lepetit, V., Fua, P.: Learning separable filters. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2754–2761 (2013)
2013
Earlier work this paper cites.
Ba, J., Caruana, R.: Do deep nets really need to be deep? In: Advances in neural information processing systems. pp. 2654–2662 (2014)
2014
Earlier work this paper cites.
Denton, E.L., Zaremba, W., Bruna, J., LeCun, Y., Fergus, R.: Exploiting linear structure within convolutional networks for efficient evaluation. In: Advances in Neural Information Processing Systems. pp. 1269–1277 (2014)
2014
Earlier work this paper cites.
2014
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.
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Li, J., Zhao, R., Huang, J.T., Gong, Y.: Learning small-size dnn with output-distribution-based criteria. In: Fifteenth Annual Conference of the International Speech Communication Association (2014)
2014
Earlier work this paper cites.
Mirza, M., Osindero, S.: Conditional generative adversarial nets (2014)
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Soudry, D., Hubara, I., Meir, R.: Expectation backpropagation: Parameter-free training of multilayer neural networks with continuous or discrete weights. In: Advances in Neural Information Processing Systems. pp. 963–971 (2014)
2014
Cited alongside, same era.
Chen, W., Wilson, J., Tyree, S., Weinberger, K., Chen, Y.: Compressing neural networks with the hashing trick. In: International Conference on Machine Learning. pp. 2285–2294 (2015)
2015
Cited alongside, same era.
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
Cited alongside, same era.
Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: International Conference on Machine Learning. pp. 1180–1189 (2015)
2015
Cited alongside, same era.
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2818–2826 (2016)
2016
Later among the works it cites.
2016
Later among the works it cites.
Wu, J., Leng, C., Wang, Y., Hu, Q., Cheng, J.: Quantized convolutional neural networks for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4820–4828 (2016)
2016
Later among the works it cites.
2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2015
Cited alongside, same era.
Han, S., Pool, J., Tran, J., Dally, W.: Learning both weights and connections for efficient neural network. In: Advances in Neural Information Processing Systems. pp. 1135–1143 (2015)
2015
Cited alongside, same era.
2015
Cited alongside, same era.
Yang, Z., Moczulski, M., Denil, M., de Freitas, N., Smola, A., Song, L., Wang, Z.: Deep fried convnets. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 1476–1483 (2015)
2015
Cited alongside, same era.
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al.: Tensorflow: A system for large-scale machine learning. In: OSDI. vol. 16, pp. 265–283 (2016)
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
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
Cited alongside, same era.
Zagoruyko, S., Komodakis, N.: Wide residual networks. arXiv preprint arXiv:1605.07146 (2016)
2016
Later among the works it cites.
2016
Later among the works it cites.
Belagiannis, V., Zisserman, A.: Recurrent human pose estimation. In: Automatic Face & Gesture Recognition (FG 2017), 2017 12th IEEE International Conference on. pp. 468–475. IEEE (2017)
2017
Later among the works it cites.
2017
Later among the works it cites.
Huang, G., Liu, S., van der Maaten, L., Weinberger, K.Q.: Condensenet: An efficient densenet using learned group convolutions. group 3
2017
Later among the works it cites.
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision (2017)
2017
Later among the works it cites.
2017
Later among the works it cites.
2017
Later among the works it cites.
Yim, J., Joo, D., Bae, J., Kim, J.: A gift from knowledge distillation: Fast optimization, network minimization and transfer learning. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
2017
Later among the works it cites.
2017
Later among the works it cites.
2017
Later among the works it cites.
Zhou, A., Yao, A., Guo, Y., Xu, L., Chen, Y.: Incremental network quantization: Towards lossless cnns with low-precision weights (2017)
2017
Later among the works it cites.
Carreira-Perpinán, M.A., Idelbayev, Y.: “learning-compression” algorithms for neural net pruning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 8532–8541 (2018)
2018
Closest in time.
2018
Closest in time.
Tung, F., Mori, G.: Clip-q: Deep network compression learning by in-parallel pruning-quantization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 7873–7882 (2018)
2018
Closest in time.
Xu, Z., Hsu, Y.C., Huang, J.: Training student networks for acceleration with conditional adversarial networks. In: BMVC. British Machine Vision Association (2018)
2018
Closest in time.