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We propose an efficient and unified framework, namely ThiNet, to simultaneously accelerate and compress CNN models in both training and inference stages.
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Exploiting linear structure within convolutional networks for efficient evaluation
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Compressing deep convolutional networks using vector quantization
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Learning distributed representations of concepts
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
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Learning both weights and connections for efficient neural network
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Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding
S. Han, H. Mao, and W. J. Dally · 2016
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Deep residual learning for image recognition
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Network trimming: A data-driven neuron pruning approach towards efficient deep architectures
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Fast convnets using group-wise brain damage
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Grad-CAM: Visual explanations from deep networks via gradient-based localization
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Guiding the long-short term memory model for image caption generation
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Learning deconvolution network for semantic segmentation
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ImageNet large scale visual recognition challenge
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Very deep convolutional networks for large-scale image recognition
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Structured transforms for small-footprint deep learning
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SqueezeNet: AlexNet-level accuracy with 50
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Learning structured sparsity in deep neural networks
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Quantized convolutional neural networks for mobile devices
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Learning deep features for discriminative localization
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Pruning filters for efficient ConvNets
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Pruning convolutional neural networks for resource efficient transfer learning
P. Molchanov, S. Tyree, T. Karras, T. Aila, and J. Kautz · 2017
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