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Adder Neural Networks (ANNs) which only contain additions bring us a new way of developing deep neural networks with low energy consumption.
Road sign classification using laplace kernel classifier
Pavel Paclık, J Novovičová, Pavel Pudil, and Petr Somol · 2000
Earlier work this paper cites.
Skewed reflected distributions generated by the laplace kernel
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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High-performance hardware for machine learning
William Dally · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Learning deconvolution network for semantic segmentation
Hyeonwoo Noh, Seunghoon Hong, and Bohyung Han · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Self-tuned deep super resolution
Zhangyang Wang, Yingzhen Yang, Zhaowen Wang, Shiyu Chang, Wei Han, Jianchao Yang, and Thomas Huang · 2015
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Deep residual learning for image recognition
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Binarized neural networks
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Sergey Zagoruyko and Nikos Komodakis · 2016
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Deep learning with low precision by half-wave gaussian quantization
Zhaowei Cai, Xiaodong He, Jian Sun, and Nuno Vasconcelos · 2017
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Like what you like: Knowledge distill via neuron selectivity transfer
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Positive-unlabeled learning with non-negative risk estimator
Ryuichi Kiryo, Gang Niu, Marthinus C Du Plessis, and Masashi Sugiyama · 2017
Earlier work this paper cites.
Towards accurate binary convolutional neural network
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Thinet: A filter level pruning method for deep neural network compression
Jian-Hao Luo, Jianxin Wu, and Weiyao Lin · 2017
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Introduction to tensor decompositions and their applications in machine learning
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Efficient processing of deep neural networks: A tutorial and survey
Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang, and Joel S Emer · 2017
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Image super-resolution via deep recursive residual network
Ying Tai, Jian Yang, and Xiaoming Liu · 2017
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Ntire 2017 challenge on single image super-resolution: Methods and results
Knowledge distillation via route constrained optimization
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Towards optimal structured cnn pruning via generative adversarial learning
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Learning instance-wise sparsity for accelerating deep models
Chuanjian Liu, Yunhe Wang, Kai Han, Chunjing Xu, and Chang Xu · 2019
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Improved knowledge distillation via teacher assistant: Bridging the gap between student and teacher
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Positive-unlabeled compression on the cloud
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A gift from knowledge distillation: Fast optimization, network minimization and transfer learning
Junho Yim, Donggyu Joo, Jihoon Bae, and Junmo Kim · 2017
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On compressing deep models by low rank and sparse decomposition
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Accelerating convolutional networks via global & dynamic filter pruning
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Discrimination-aware channel pruning for deep neural networks
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Snapshot distillation: Teacher-student optimization in one generation
Chenglin Yang, Lingxi Xie, Chi Su, and Alan L Yuille · 2019
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Legonet: Efficient convolutional neural networks with lego filters
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Optical flow distillation: Towards efficient and stable video style transfer
Xinghao Chen, Yiman Zhang, Yunhe Wang, Han Shu, Chunjing Xu, and Chang Xu · 2020
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Logdet metric-based domain adaptation
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Efficient residual dense block search for image super-resolution
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