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Model inversion, whose goal is to recover training data from a pre-trained model, has been recently proved feasible.
Discovery of a perceptual distance function for measuring image similarity
Beitao Li, Edward Chang, and Yi Wu · 2003
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton · 2012
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, S. Jha, and T. Ristenpart · 2015
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Deep residual learning for image recognition. corr abs/1512.03385 (2015)
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Distilling the knowledge in a neural network
Geoffrey E. Hinton, Oriol Vinyals, and Jeffrey Dean · 2015
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Tiny imagenet visual recognition challenge
Ya Le and Xuan Yang · 2015
Earlier work this paper cites.
Understanding deep image representations by inverting them
Aravindh Mahendran and Andrea Vedaldi · 2015
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Shaoqing Ren, Kaiming He, Ross B Girshick, and Jian Sun · 2015
Earlier work this paper cites.
A methodology for formalizing model-inversion attacks
Xi Wu, Matthew Fredrikson, Somesh Jha, and Jeffrey F Naughton · 2016
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Sergey Zagoruyko and Nikos Komodakis · 2016
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Data-free knowledge distillation for deep neural networks
Raphael Gontijo Lopes, Stefano Fenu, and Thad Starner · 2017
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On compressing deep models by low rank and sparse decomposition
Xiyu Yu, Tongliang Liu, Xinchao Wang, and Dacheng Tao · 2017
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Unsupervised feature learning via non-parametric instance-level discrimination
Zhirong Wu, Yuanjun Xiong, Stella Yu, and Dahua Lin · 2018
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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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Data-free network quantization with adversarial knowledge distillation
Yoojin Choi, Jihwan Choi, Mostafa El-Khamy, and Jungwon Lee · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Large-scale generative data-free distillation
Liangchen Luo, Mark Sandler, Zi Lin, Andrey Zhmoginov, and Andrew Howard · 2020
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Adversarial self-supervised data-free distillation for text classification
Xinyin Ma, Yongliang Shen, Gongfan Fang, Chen Chen, Chenghao Jia, and Weiming Lu · 2020
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Factorizable graph convolutional networks, 2020
Yiding Yang, Zunlei Feng, Mingli Song, and Xinchao Wang · 2020
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Data-free adversarial distillation
Gongfan Fang, Jie Song, Chengchao Shen, Xinchao Wang, Da Chen, and Mingli Song · 2019
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Zero-shot knowledge transfer via adversarial belief matching
Paul Micaelli and Amos J Storkey · 2019
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Amalgamating knowledge towards comprehensive classification
Chengchao Shen, Xinchao Wang, Jie Song, Li Sun, and Mingli Song · 2019
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Student becoming the master: Knowledge amalgamation for joint scene parsing, depth estimation, and more
Jingwen Ye, Yixin Ji, Xinchao Wang, Kairi Ou, Dapeng Tao, and Mingli Song · 2019
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Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
Cited alongside, same era.
Data-free knowledge amalgamation via group-stack dual-gan
Jingwen Ye, Yixin Ji, Xinchao Wang, Xin Gao, and Mingli Song · 2020
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Dreaming to distill: Data-free knowledge transfer via deepinversion
Hongxu Yin, Pavlo Molchanov, Jose M Alvarez, Zhizhong Li, Arun Mallya, Derek Hoiem, Niraj K Jha, and Jan Kautz · 2020
Later among the works it cites.
The secret revealer: generative model-inversion attacks against deep neural networks
Yuheng Zhang, Ruoxi Jia, Hengzhi Pei, Wenxiao Wang, Bo Li, and Dawn Song · 2020
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Overcoming catastrophic forgetting in graph neural networks
Huihui Liu, Yiding Yang, and Xinchao Wang · 2021
Closest in time.
Distilling knowledge from graph convolutional networks, 2021
Yiding Yang, Jiayan Qiu, Mingli Song, Dacheng Tao, and Xinchao Wang · 2021
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