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Learning portable neural networks is very essential for computer vision for the purpose that pre-trained heavy deep models can be well applied on edge devices such as mobile phones and micro sensors.
A database for handwritten text recognition research
Jonathan J. Hull · 1994
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Gradient-based learning applied to document recognition
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Deep inside convolutional networks: Visualising image classification models and saliency maps
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Exploiting linear structure within convolutional networks for efficient evaluation
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Compressing neural networks with the hashing trick
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Song Han, Huizi Mao, and William J Dally · 2015
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Unsupervised and semi-supervised learning with categorical generative adversarial networks
Xnor-net: Imagenet classification using binary convolutional neural networks
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Cnnpack: packing convolutional neural networks in the frequency domain
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Towards interpretable deep neural networks by leveraging adversarial examples
Yinpeng Dong, Hang Su, Jun Zhu, and Fan Bao · 2017
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Subic: A supervised, structured binary code for image search
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Mimicking very efficient network for object detection
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Semi-supervised learning with generative adversarial networks
Augustus Odena · 2016
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Grad-cam: Visual explanations from deep networks via gradient-based localization
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A gift from knowledge distillation: Fast optimization, network minimization and transfer learning
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Interpretable convolutional neural networks
Quanshi Zhang, Ying Nian Wu, and Song-Chun Zhu · 2018
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Legonet: Efficient convolutional neural networks with lego filters
Zhaohui Yang, Yunhe Wang, Chuanjian Liu, Hanting Chen, Chunjing Xu, Boxin Shi, Chao Xu, and Chang Xu · 2019
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