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Quantization and pruning are core techniques used to reduce the inference costs of deep neural networks.
Qgan: Quantized generative adversarial networks
Wang, P., Wang, D., Ji, Y., Xie, X., Song, H., Liu, X., Lyu, Y., and Xie, Y. (2019) · 1901
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The state of sparsity in deep neural networks
Gale, T., Elsen, E., and Hooker, S. (2019) · 1902
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Understanding straight-through estimator in training activation quantized neural nets
Yin, P., Lyu, J., Zhang, S., Osher, S., Qi, Y., and Xin, J. (2019) · 1903
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Sparse networks from scratch: Faster training without losing performance
Dettmers, T. and Zettlemoyer, L. (2019) · 1907
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LSQ+: improving low-bit quantization through learnable offsets and better initialization
Bhalgat, Y., Lee, J., Nagel, M., Blankevoort, T., and Kwak, N. (2020) · 2004
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Integer quantization for deep learning inference: Principles and empirical evaluation
Wu, H., Judd, P., Zhang, X., Isaev, M., and Micikevicius, P. (2020) · 2004
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Bayesian bits: Unifying quantization and pruning
van Baalen, M., Louizos, C., Nagel, M., Amjad, R. A., Wang, Y., Blankevoort, T., and Welling, M. (2020) · 2005
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Ultra low-latency, low-area inference accelerators using heterogeneous deep quantization with qkeras and hls4ml
Coelho Jr, C. N., Kuusela, A., Zhuang, H., Aarrestad, T., Loncar, V., Ngadiuba, J., Pierini, M., and Summers, S. (2020) · 2006
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al. (2009) · 2009
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The pascal visual object classes (voc) challenge
Everingham, M., Van Gool, L., Williams, C. K., Winn, J., and Zisserman, A. (2010) · 2010
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Understanding the difficulty of training deep feedforward neural networks
Glorot, X. and Bengio, Y. (2010) · 2010
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Image super-resolution via sparse representation
Yang, J., Wright, J., Huang, T. S., and Ma, Y. (2010) · 2010
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Joint pruning & quantization for extremely sparse neural networks
Yu, P.-H., Wu, S.-S., Klopp, J. P., Chen, L.-G., and Chien, S.-Y. (2020) · 2010
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Deep sparse rectifier neural networks
Glorot, X., Bordes, A., and Bengio, Y. (2011) · 2011
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Low-complexity single-image super-resolution based on nonnegative neighbor embedding
Bevilacqua, M., Roumy, A., Guillemot, C., and Alberi-Morel, M. L. (2012) · 2012
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Neural networks for machine learning
Hinton, G., Srivastava, N., and Swersky, K. (2012) · 2012
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1.1 computing’s energy problem (and what we can do about it)
Horowitz, M. (2014) · 2014
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Han, S., Mao, H., and Dally, W. J. (2015) · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., and Sun, J. (2015) · 2015
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T. (2015) · 2015
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ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L. (2015) · 2015
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Empirical evaluation of rectified activations in convolutional network
Xu, B., Wang, N., Chen, T., and Li, M. (2015) · 2015
Cited alongside, same era.
Towards the limit of network quantization
Choi, Y., El-Khamy, M., and Lee, J. (2016) · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
Cited alongside, same era.
Network trimming: A data-driven neuron pruning approach towards efficient deep architectures
Hu, H., Peng, R., Tai, Y.-W., and Tang, C.-K. (2016) · 2016
Cited alongside, same era.
Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network
Shi, W., Caballero, J., Huszár, F., Totz, J., Aitken, A. P., Bishop, R., Rueckert, D., and Wang, Z. (2016) · 2016
Mobilenetv2: Inverted residuals and linear bottlenecks
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018) · 2018
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How does batch normalization help optimization?
Santurkar, S., Tsipras, D., Ilyas, A., and Mądry, A. (2018) · 2018
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Deep relu networks have surprisingly few activation patterns
Hanin, B. and Rolnick, D. (2019) · 2019
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Rethinking imagenet pre-training
He, K., Girshick, R., and Dollár, P. (2019) · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. (2019) · 2019
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Autoprune: Automatic network pruning by regularizing auxiliary parameters
Xiao, X. and Wang, Z. (2019) · 2019
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Cited alongside, same era.
Learning Deep Features for Discriminative Localization
Zhou, B., Khosla, A., A., L., Oliva, A., and Torralba, A. (2016) · 2016
Cited alongside, same era.
Gpu kernels for block-sparse weights
Gray, S., Radford, A., and Kingma, D. P. (2017) · 2017
Cited alongside, same era.
Deep learning scaling is predictable, empirically
Hestness, J., Narang, S., Ardalani, N., Diamos, G., Jun, H., Kianinejad, H., Patwary, M., Ali, M., Yang, Y., and Zhou, Y. (2017) · 2017
Cited alongside, same era.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S. (2017) · 2017
Cited alongside, same era.
Quantized neural networks: Training neural networks with low precision weights and activations
Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R., and Bengio, Y. (2017) · 2017
Cited alongside, same era.
Image-to-image translation with conditional adversarial networks
Isola, P., Zhu, J.-Y., Zhou, T., and Efros, A. A. (2017) · 2017
Cited alongside, same era.
A faster pytorch implementation of faster r-cnn
Yang, J., Lu, J., Batra, D., and Parikh, D. (2017) · 2017
Cited alongside, same era.
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Universal deep neural network compression
Choi, Y., El-Khamy, M., and Lee, J. (2020) · 2020
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Quantisation and pruning for neural network compression and regularisation
Paupamah, K., James, S., and Klein, R. (2020) · 2020
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Structured feature sparsity training for convolutional neural network compression
Wang, W. and Zhu, L. (2020) · 2020
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Automatic neural network compression by sparsity-quantization joint learning: A constrained optimization-based approach
Yang, H., Gui, S., Zhu, Y., and Liu, J. (2020) · 2020
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Sparse-gan: Sparsity-constrained generative adversarial network for anomaly detection in retinal oct image
Zhou, K., Gao, S., Cheng, J., Gu, Z., Fu, H., Tu, Z., Yang, J., Zhao, Y., and Liu, J. (2020) · 2020
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yjn870/espcn-pytorch: Pytorch implementation of real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network (cvpr 2016)
(2019) · 2021
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kuangliu/pytorch-cifar: 95.47% on cifar10 with pytorch
(2021) · 2021
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torch.nn.qat — pytorch 1.9.0 documentation
(2021) · 2021
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An energy-efficient edge computing paradigm for convolution-based image upsampling
Colbert, I., Kreutz-Delgado, K., and Das, S. (2021) · 2021
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A survey of quantization methods for efficient neural network inference
Gholami, A., Kim, S., Dong, Z., Yao, Z., Mahoney, M. W., and Keutzer, K. (2021) · 2021
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Hoefler, T., Alistarh, D., Ben-Nun, T., Dryden, N., and Peste, A. (2021) · 2021
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Data-type aware arithmetic intensity for deep neural networks
Jha, N. K., Mittal, S., and Avancha, S. (2021) · 2021
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Pruning and quantization for deep neural network acceleration: A survey
Liang, T., Glossner, J., Wang, L., Shi, S., and Zhang, X. (2021) · 2021
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Xilinx/brevitas
Pappalardo, A. (2021) · 2021
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