Mixmatch: A holistic approach to semi-supervised learning, 2019. ,
Model compression as constrained optimization, with application to neural nets. part ii: quantization, 2017. ,
A survey of model compression and acceleration for deep neural networks, 2017. ,
Towards the limit of network quantization, 2016. ,
Binaryconnect: Training deep neural networks with binary weights during propagations, 2015. ,
ImageNet: A Large-Scale Hierarchical Image Database, Conference on Computer Vision and Pattern Recognition, 2009. ,
Exploiting linear structure within convolutional networks for efficient evaluation, Advances in Neural Information Processing Systems 27, 2014. ,
Optimized product quantization, IEEE Trans. Pattern Anal. Mach. Intell, 2014. ,
Compressing deep convolutional networks using vector quantization, 2014. ,
A survey on methods and theories of quantized neural networks, 2018. ,
Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding, International Conference on Learning Representations, 2016. ,
Deep residual learning for image recognition, 2015. ,
Mask r-cnn, International Conference on Computer Vision (ICCV, 2017. ,
Distilling the knowledge in a neural network, NIPS Deep Learning Workshop, 2014. ,
, , 2019.
, Densely connected convolutional networks. Conference on Computer Vision and Pattern Recognition, 2017.
Squeezenet: Alexnet-level accuracy with 50x fewer parameters and ¡0.5mb model size, 2016. ,
Product quantization for nearest neighbor search, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011. ,
URL : https://hal.archives-ouvertes.fr/inria-00514462
Product Quantization for Nearest Neighbor Search, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011. ,
Imagenet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems, 2012. ,
Optimal brain damage, Advances in Neural Information Processing Systems, 1990. ,
Ternary weight networks, 2016. ,
Towards accurate binary convolutional neural network, 2017. ,
Learning efficient convolutional networks through network slimming, International Conference on Computer Vision, 2017. ,
Data-free knowledge distillation for deep neural networks, 2017. ,
Thinet: A filter level pruning method for deep neural network compression, 2017. ,
Shufflenet V2: practical guidelines for efficient CNN architecture design, 2018. ,
Ashwin Bharambe, and Laurens van der Maaten. Exploring the limits of weakly supervised pretraining, 2018. ,
Training wide residual networks for deployment using a single bit for each weight, 2018. ,
Apprentice: Using knowledge distillation techniques to improve low-precision network accuracy, 2017. ,
WRPN: wide reduced-precision networks, 2017. ,
Cartesian k-means, Conference on Computer Vision and Pattern Recognition, 2013. ,
Xnor-net: Imagenet classification using binary convolutional neural networks, European Conference on Computer Vision, 2016. ,
Inverted residuals and linear bottlenecks: Mobile networks for classification, detection and segmentation, 2018. ,
Learning discrete weights using the local reparameterization trick, 2017. ,
Rethinking model scaling for convolutional neural networks, 2019. ,
, The new data and new challenges in multimedia research, 2015.
Deep neural network compression by in-parallel pruningquantization, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018. ,
HAQ: hardware-aware automated quantization, 2018. ,
Haq: hardware-aware automated quantization, 2018. ,
Quantized convolutional neural networks for mobile devices, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. ,
Aggregated residual transformations for deep neural networks, Conference on Computer Vision and Pattern Recognition, 2017. ,
Billion-scale semisupervised learning for image classification, 2019. ,
Shufflenet: An extremely efficient convolutional neural network for mobile devices, 2017. ,
Incremental network quantization: Towards lossless cnns with low-precision weights, 2017. ,
Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients, 2016. ,
Trained ternary quantization, 2016. ,
Learning transferable architectures for scalable image recognition, 2017. ,