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We study the use of knowledge distillation to compress the U-net architecture.
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Ioffe, S. and Szegedy, C., 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv
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Song Han, Jeff Pool, John Tran, and William Dally. Learning both weights and connections for efficient neural network. In Advances in neural information processing systems
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Luo, P., Zhu, Z., Liu, Z., Wang, X. and Tang, X., 2016, February. Face Model Compression by Distilling Knowledge from Neurons. In proceedings of Association for the Advancement of Artificial Intelligence
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Cited alongside, same era.
Rastegari, M., Ordonez, V., Redmon, J. and Farhadi, A., 2016, October. Xnor-net: Imagenet classification using binary convolutional neural networks. In European Conference on Computer Vision
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Alvarez, J.M. and Salzmann, M., 2017. Compression-aware training of deep networks. In Advances in Neural Information Processing Systems
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Chen, G., Choi, W., Yu, X., Han, T. and Chandraker, M., 2017. Learning efficient object detection models with knowledge distillation. In Advances in Neural Information Processing Systems
2017
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Wang, C., Lan, X. and Zhang, Y., 2017. Model Distillation with Knowledge Transfer from Face Classification to Alignment and Verification. arXiv preprint arXiv
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Newell, A., Yang, K. and Deng, J., 2016, October. Stacked hourglass networks for human pose estimation. In European Conference on Computer Vision
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