SqueezeNet
论文标题:SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size
论文:
Iandola F N, Han S, Moskewicz M W, et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size[J]. arXiv preprint arXiv:1602.07360, 2016.
论文链接:https://arxiv.org/pdf/1602.07360.pdf
ShuffleNet
ShuffleNet V1
论文标题:Shufflenet: An extremely efficient convolutional neural network for mobile devices
论文:
Zhang X, Zhou X, Lin M, et al. Shufflenet: An extremely efficient convolutional neural network for mobile devices[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 6848-6856.
ShuffleNet V2
论文标题:Shufflenet v2: Practical guidelines for efficient cnn architecture design
论文:
Ma N, Zhang X, Zheng H T, et al. Shufflenet v2: Practical guidelines for efficient cnn architecture design[C]//Proceedings of the European conference on computer vision (ECCV). 2018: 116-131.
MobileNet
MobileNet系列详解可参考:https://blog.51cto.com/u_15671528/5528810
MobileNet V1
论文标题:Efficient convolutional neural networks for mobile vision applications
论文:
Howard A G, Zhu M, Chen B, et al. Mobilenets: Efficient convolutional neural networks for mobile vision applications[J]. arXiv preprint arXiv:1704.04861, 2017.
论文链接:https://arxiv.org/pdf/1704.04861.pdf
MobileNet V2
论文标题:Mobilenetv2: Inverted residuals and linear bottlenecks
论文:
Sandler M, Howard A, Zhu M, et al. Mobilenetv2: Inverted residuals and linear bottlenecks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 4510-4520.
MobileNet V3
论文标题:Searching for mobilenetv3
论文:
Howard A, Sandler M, Chu G, et al. Searching for mobilenetv3[C]//Proceedings of the IEEE/CVF international conference on computer vision. 2019: 1314-1324.
Xception
论文标题:Xception: Deep learning with depthwise separable convolutions
论文:
Chollet F. Xception: Deep learning with depthwise separable convolutions[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 1251-1258.
EfficientNet
论文标题:Efficientnet: Rethinking model scaling for convolutional neural networks
论文:
Tan M, Le Q. Efficientnet: Rethinking model scaling for convolutional neural networks[C]//International conference on machine learning. PMLR, 2019: 6105-6114.
论文链接:http://proceedings.mlr.press/v97/tan19a/tan19a.pdf
GhostNet
论文标题:Ghostnet: More features from cheap operations
论文:
Han K, Wang Y, Tian Q, et al. Ghostnet: More features from cheap operations[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 1580-1589.
RegNet
论文标题:Designing network design spaces
论文:
Radosavovic I, Kosaraju R P, Girshick R, et al. Designing network design spaces[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 10428-10436.
MobileViT
MobileViT V1
论文标题:Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer
论文:
Mehta S, Rastegari M. Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer[J]. arXiv preprint arXiv:2110.02178, 2021.
论文链接:https://arxiv.org/pdf/2110.02178.pdf
MobileViT V2
论文标题:Separable self-attention for mobile vision transformers
论文:
Mehta S, Rastegari M. Separable self-attention for mobile vision transformers[J]. arXiv preprint arXiv:2206.02680, 2022.
论文链接:https://arxiv.org/pdf/2206.02680.pdf
MobileViT V3
论文标题:Mobilevitv3: Mobile-friendly vision transformer with simple and effective fusion of local, global and input features
论文:
Wadekar S N, Chaurasia A. Mobilevitv3: Mobile-friendly vision transformer with simple and effective fusion of local, global and input features[J]. arXiv preprint arXiv:2209.15159, 2022.
论文链接:https://arxiv.org/pdf/2209.15159.pdf
参考链接
本文作者:StubbornHuang
版权声明:本文为站长原创文章,如果转载请注明原文链接!
原文标题:深度学习 – 归纳轻量级神经网络(长期更新)
原文链接:https://www.stubbornhuang.com/2544/
发布于:2023年03月17日 10:58:29
修改于:2023年06月21日 17:04:08
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