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Black-Box Knowledge Distillation (B2KD) is a formulated problem for cloud-to-edge model compression with invisible data and models hosted on the server.
The johnson-lindenstrauss lemma and the sphericity of some graphs
Peter Frankl and Hiroshi Maehara · 1988
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Nonsmooth duality, sandwich, and squeeze theorems
Adrian Stephen Lewis and RE Lucchetti · 2000
Earlier work this paper cites.
A global geometric framework for nonlinear dimensionality reduction
Joshua B Tenenbaum, Vin de Silva, and John C Langford · 2000
Earlier work this paper cites.
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Mario Köppen · 2002
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Envelope theorems for arbitrary choice sets
Paul Milgrom and Ilya Segal · 2002
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On a constructive proof of kolmogorov’s superposition theorem
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Optimal transport: old and new
Cédric Villani · 2009
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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Information-theoretical learning of discriminative clusters for unsupervised domain adaptation
Yuan Shi and Fei Sha · 2012
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Do deep nets really need to be deep?
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, Jeff Dean, et al · 2015
Earlier work this paper cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
Earlier work this paper cites.
InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
Earlier work this paper cites.
Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
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Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
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Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
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Zero-shot knowledge transfer via adversarial belief matching
Paul Micaelli and Amos J Storkey · 2019
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Knockoff nets: Stealing functionality of black-box models
Tribhuvanesh Orekondy, Bernt Schiele, and Mario Fritz · 2019
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Meal: Multi-model ensemble via adversarial learning
Zhiqiang Shen, Zhankui He, and Xiangyang Xue · 2019
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Knowledge distillation from internal representations
Gustavo Aguilar, Yuan Ling, Yu Zhang, Benjamin Yao, Xing Fan, and Chenlei Guo · 2020
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Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings
Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger · 2020
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Learning student networks via feature embedding
Hanting Chen, Yunhe Wang, Chang Xu, Chao Xu, and Dacheng Tao · 2020
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Weihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto, and Masashi Sugiyama · 2017
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Paying more attention to attention: improving the performance of convolutional neural networks via attention transfer
Nikos Komodakis and Sergey Zagoruyko · 2017
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Unsupervised anomaly detection with generative adversarial networks to guide marker discovery
Thomas Schlegl, Philipp Seeböck, Sebastian M Waldstein, Ursula Schmidt-Erfurth, and Georg Langs · 2017
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Training shallow and thin networks for acceleration via knowledge distillation with conditional adversarial networks
Zheng Xu, Yen-Chang Hsu, and Jiawei Huang · 2017
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A gift from knowledge distillation: Fast optimization, network minimization and transfer learning
Junho Yim, Donggyu Joo, Jihoon Bae, and Junmo Kim · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
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Adversarial network compression
Vasileios Belagiannis, Azade Farshad, and Fabio Galasso · 2018
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Group knowledge transfer: Federated learning of large cnns at the edge
Chaoyang He, Murali Annavaram, and Salman Avestimehr · 2020
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A geometric understanding of deep learning
Na Lei, Dongsheng An, Yang Guo, Kehua Su, Shixia Liu, Zhongxuan Luo, Shing-Tung Yau, and Xianfeng Gu · 2020
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Improved knowledge distillation via teacher assistant
Seyed Iman Mirzadeh, Mehrdad Farajtabar, Ang Li, Nir Levine, Akihiro Matsukawa, and Hassan Ghasemzadeh · 2020
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Spatio-temporal graph for video captioning with knowledge distillation
Boxiao Pan, Haoye Cai, De-An Huang, Kuan-Hui Lee, Adrien Gaidon, Ehsan Adeli, and Juan Carlos Niebles · 2020
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Heterogeneous knowledge distillation using information flow modeling
Nikolaos Passalis, Maria Tzelepi, and Anastasios Tefas · 2020
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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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Knowledge distillation: A survey
Jianping Gou, Baosheng Yu, Stephen J Maybank, and Dacheng Tao · 2021
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Quped: Quantized personalization via distillation with applications to federated learning
Kaan Ozkara, Navjot Singh, Deepesh Data, and Suhas Diggavi · 2021
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Alp-kd: Attention-based layer projection for knowledge distillation
Peyman Passban, Yimeng Wu, Mehdi Rezagholizadeh, and Qun Liu · 2021
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Does knowledge distillation really work?
Samuel Stanton, Pavel Izmailov, Polina Kirichenko, Alexander A Alemi, and Andrew G Wilson · 2021
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Zero-shot knowledge distillation from a decision-based black-box model
Zi Wang · 2021
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Decoupled knowledge distillation
Borui Zhao, Quan Cui, Renjie Song, Yiyu Qiu, and Jiajun Liang · 2022
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