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Deep Neural Networks (DNNs) are ubiquitous in today's computer vision land-scape, despite involving considerable computational costs.
Least squares quantization in pcm
Lloyd, S · 1982
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ImageNet: A Large-Scale Hierarchical Image Database
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Learning multiple layers of features from tiny images
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Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., et al · 2014
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Data-free parameter pruning for deep neural networks
Srinivas, S. and Babu, R. V · 2015
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Eie: efficient inference engine on compressed deep neural network
Han, S. et al · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., et al · 2016
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Wide residual networks
Zagoruyko, S. and Komodakis, N · 2016
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Chen, L.-C. et al · 2017
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A survey of model compression and acceleration for deep neural networks
Cheng, Y., Wang, D., et al · 2017
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Mask r-cnn
He, K., Gkioxari, G., et al · 2017
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Pruning filters for efficient convnets
Li, H. et al · 2017
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Thinet: A filter level pruning method for deep neural network compression
Luo, J.-H., Wu, J., and Lin, W · 2017
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An equivalence of fully connected layer and convolutional layer
Ma, W. and Lu, J · 2017
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Frankle, J. and Carbin, M · 2018
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Soft filter pruning for accelerating deep convolutional neural networks
He, Y., Kang, G., et al · 2018
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Data-driven sparse structure selection for deep neural networks
Huang, Z. and Wang, N · 2018
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Rethinking the value of network pruning
Liu, Z., Sun, M., et al · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
Sandler, M., Howard, A., et al · 2018
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Improving neural network quantization without retraining using outlier channel splitting
Zhao, R., Hu, Y., Dotzel, J., De Sa, C., and Zhang, Z · 2019
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Neuron merging: Compensating for pruned neurons
Kim, W., Kim, S., et al · 2020
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A signal propagation perspective for pruning neural networks at initialization
Lee, N., Ajanthan, T., et al · 2020
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Provable filter pruning for efficient neural networks
Liebenwein, L., Baykal, C., et al · 2020
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Pruning filter in filter
Meng, F., Cheng, H., et al · 2020
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Lookahead: a far-sighted alternative of magnitude-based pruning
Park, S., Lee, J., et al · 2020
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Redundant feature pruning for accelerated inference in deep neural networks
Ayinde, B. O. et al · 2019
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The state of sparsity in deep neural networks
Gale, T., Elsen, E., and Hooker, S · 2019
Cited alongside, same era.
Searching for mobilenetv3
Howard, A. et al · 2019
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Towards optimal structured cnn pruning via generative adversarial learning
Lin, S., Ji, R., et al · 2019
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Same, same but different: Recovering neural network quantization error through weight factorization
Meller, E., Finkelstein, A., Almog, U., and Grobman, M · 2019
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Data-free quantization through weight equalization and bias correction
Nagel, M., Baalen, M. v., et al · 2019
Cited alongside, same era.
Comparing rewinding and fine-tuning in neural network pruning
Renda, A., Frankle, J., and Carbin, M · 2020
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And the bit goes down: Revisiting the quantization of neural networks
Stock, P. et al · 2020
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Pruning neural networks without any data by iteratively conserving synaptic flow
Tanaka, H., Kunin, D., et al · 2020
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Scop: Scientific control for reliable neural network pruning
Tang, Y., Wang, Y., et al · 2020
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Dreaming to distill: Data-free knowledge transfer via deepinversion
Yin, H., Molchanov, P., et al · 2020
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Neuron-level structured pruning using polarization regularizer
Zhuang, T., Zhang, Z., et al · 2020
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