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Recently, there have been increasing demands to construct compact deep architectures to remove unnecessary redundancy and to improve the inference speed.
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HD-CNN: hierarchical deep convolutional neural networks for large scale visual recognition
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Compression of deep convolutional neural networks for fast and low power mobile applications
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FractalNet: Ultra-deep neural networks without residuals
G. Larsson, M. Maire, and G. Shakhnarovich · 2016
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SplitNet: Learning to semantically split deep networks for parameter reduction and model parallelization
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T.-J. Yang, Y.-H. Chen, and V. Sze · 2017
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On compressing deep models by low rank and sparse decomposition
X. Yu, T. Liu, X. Wang, and D. Tao · 2017
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