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- Forrest N. Iandola, Song Han, et al. "SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size." (2017). [pdf] (SqueezeNet)
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- Howard, Andrew G, et al. "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications." (2017). [pdf] (MobileNets)
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- Zhang, Xiangyu, et al. "ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices." (2017). [pdf] (ShuffleNet)
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- Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Quoc V. Le "Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation." (2018). [pdf] (MobileNetV2)
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- Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen et al. "IMnasNet: Platform-Aware Neural Architecture Search for Mobile." (2018). [pdf] (IMnasNet)
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- Geoffrey Hinton, Oriol Vinyals, Jeff Dean. "Distilling the Knowledge in a Neural Network." CVPR(2014). [pdf] (Distilling)
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- Romero, Adriana, et al. "FitNets: Hints for Thin Deep Nets.." Computer Science (2014). [pdf] (FitNets)
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- Zhou, Guorui, et al. "Rocket Launching: A Universal and Efficient Framework for Training Well-performing Light Net." (2017). [pdf] (Launching)
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- Junho Yim, Donggyu Joo,Jihoon Bae, Junmo Kim et al. "A Gift from Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning." CVPR(2017). [pdf]
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- Q Li, S Jin, J Yan et al. "Mimicking Very Efficient Network for Object Detection." CVPR(2017). [pdf] (Mimicking)
Review
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- Yu Cheng, Duo Wang, et al. "A Survey of Model Compression and Acceleration for Deep Neural Networks." IEEE Signal Processing Magazine(2017). [pdf]