深度学习模型参数很多（模型很大）是制约深度学习模型部署在移动端一个很大的瓶颈，最近有不少轻量级的深度学习模型提出，以下是对一些经典轻量级深度学习模型的总结：
1、Squeezenet:Alexnet-level accuracy with 50x fewer parameters and¡ 1mb model size.
arXiv preprint arXiv:1602.07360, 2016
2、MobileNets:Efficient Convolutional Neural Networks for Mobile Vision Applications
arXiv:1704.04861v1 [cs.CV] 17 Apr 2017
3、Densely ConnectedConvolutional Networks
arXiv:1608.06993v4 [cs.CV] 27 Aug 2017
4、Xnornet: Imagenetclassification using binary convolutional neural networks.
arXiv preprint arXiv:1603.05279, 2016
5、Quantized convolutionalneural networks for mobile devices
arXiv preprint arXiv:1512.06473, 2015
6、Xception: DeepLearning with Depthwise Separable Convolutions
arXiv:1610.02357v3 [cs.CV] 4 Apr 2017
7、ProjectionNet: LearningEfficient On-Device Deep Networks Using Neural Projections
arXiv:1708.00630v2 [cs.LG] 9 Aug 2017
8、Factorizedconvolutional neural networks
arXiv preprint arXiv:1608.04337, 2016
9、LearningTransferable Architectures for Scalable Image Recognition
atXiv:1707.07012v2 [cs.CV] 25 Oct 2017
10、Structured transformsfor small-footprint deep learning.
11、Deep compression:Compressing deep neural network with pruning, trained quantization and huffmancoding
12、Quantized neuralnetworks: Training neural networks with low precision weights and activations.
arXivpreprint arXiv:1609.07061, 2016