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Knowledge Distillation is an effective method to transfer the learning across deep neural networks.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Model compression
Cristian Buciluǎ, Rich Caruana, and Alexandru Niculescu-Mizil · 2006
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Reading digits in natural images with unsupervised feature learning, 2011
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2014
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Tiny imagenet visual recognition challenge
Ya Le and Xuan Yang · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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An efficient algorithm for calculating the exact hausdorff distance
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Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
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Data-free knowledge distillation for deep neural networks
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Fruit recognition from images using deep learning
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Dream distillation: A data-independent model compression framework
Kartikeya Bhardwaj, Naveen Suda, and Radu Marculescu · 2019
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Data-free learning of student networks
Hanting Chen, Yunhe Wang, Chang Xu, Zhaohui Yang, Chuanjian Liu, Boxin Shi, Chunjing Xu, Chao Xu, and Qi Tian · 2019
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Mid-air: A multi-modal dataset for extremely low altitude drone flights
Michael Fonder and Marc Van Droogenbroeck · 2019
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Zero-shot knowledge transfer via adversarial belief matching
Paul Micaelli and Amos J Storkey · 2019
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Zero-shot knowledge distillation in deep networks
Gaurav Kumar Nayak, Konda Reddy Mopuri, Vaisakh Shaj, Venkatesh Babu Radhakrishnan, and Anirban Chakraborty · 2019
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Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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DeGAN : Data‐Enriching gan for retrieving representative samples from a trained classifier
Sravanti Addepalli, Gaurav Kumar Nayak, Anirban Chakraborty, and R. Venkatesh Babu · 2020
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