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We propose a novel clustering mechanism based on an incompatibility property between subsets of data that emerges during model training.
Experiments with a new boosting algorithm
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A clustering method based on boosting
D Frossyniotis, Aristidis Likas, and Andreas Stafylopatis · 2004
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Co-training and expansion: Towards bridging theory and practice
Maria-Florina Balcan, Avrim Blum, and Ke Yang · 2005
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Probabilistic boosting-tree: learning discriminative models for classification, recognition, and clustering
Zhuowen Tu · 2005
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Boostcluster: boosting clustering by pairwise constraints
Yi Liu, Rong Jin, and Anil K Jain · 2007
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Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
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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 and Geoffrey Hinton · 2009
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Self-paced learning for latent variable models
M Kumar, Benjamin Packer, and Daphne Koller · 2010
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Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition
J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel · 2011
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A comprehensive survey of clustering algorithms
Dongkuan Xu and Yingjie Tian · 2015
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Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
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What objective does self-paced learning indeed optimize?
Deyu Meng, Qian Zhao, and Lu Jiang · 2016
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Targeted backdoor attacks on deep learning systems using data poisoning
Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song · 2017
Cited alongside, same era.
Badnets: Identifying vulnerabilities in the machine learning model supply chain
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg · 2017
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Certified defenses for data poisoning attacks
Jacob Steinhardt, Pang Wei W Koh, and Percy S Liang · 2017
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Turning your weakness into a strength: Watermarking deep neural networks by backdooring
Yossi Adi, Carsten Baum, Moustapha Cisse, Benny Pinkas, and Joseph Keshet · 2018
Clustergan: Latent space clustering in generative adversarial networks
Sudipto Mukherjee, Himanshu Asnani, Eugene Lin, and Sreeram Kannan · 2019
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Adversarial robustness toolbox v1.0.0
Maria-Irina Nicolae, Mathieu Sinn, Minh Ngoc Tran, Beat Buesser, Ambrish Rawat, Martin Wistuba, Valentina Zantedeschi, Nathalie Baracaldo, Bryant Chen, Heiko Ludwig, Ian M. Molloy, and Ben Edwards · 2019
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Learning with bad training data via iterative trimmed loss minimization
Yanyao Shen and Sujay Sanghavi · 2019
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Deep clustering by gaussian mixture variational autoencoders with graph embedding
Linxiao Yang, Ngai-Man Cheung, Jiaying Li, and Jun Fang · 2019
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Witches’ brew: Industrial scale data poisoning via gradient matching
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Detecting backdoor attacks on deep neural networks by activation clustering
Bryant Chen, Wilka Carvalho, Nathalie Baracaldo, Heiko Ludwig, Benjamin Edwards, Taesung Lee, Ian Molloy, and Biplav Srivastava · 2018
Cited alongside, same era.
Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels
Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, and Li Fei-Fei · 2018
Cited alongside, same era.
Spectral signatures in backdoor attacks
Brandon Tran, Jerry Li, and Aleksander Madry · 2018
Cited alongside, same era.
Clean-label backdoor attacks
Alexander Turner, Dimitris Tsipras, and Aleksander Madry · 2018
Cited alongside, same era.
Strip: A defence against trojan attacks on deep neural networks
Yansong Gao, Change Xu, Derui Wang, Shiping Chen, Damith C. Ranasinghe, and Surya Nepal · 2019
Cited alongside, same era.
Badnets: Identifying vulnerabilities in the machine learning model supply chain
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg · 2019
Cited alongside, same era.
Imagenette: A smaller subset of 10 easily classified classes from imagenet, March 2019
Jeremy Howard · 2019
Cited alongside, same era.
Jonas Geiping, Liam Fowl, W Ronny Huang, Wojciech Czaja, Gavin Taylor, Michael Moeller, and Tom Goldstein · 2020
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Self-paced robust learning for leveraging clean labels in noisy data
Xuchao Zhang, Xian Wu, Fanglan Chen, Liang Zhao, and Chang-Tien Lu · 2020
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Manipulating machine learning: Poisoning attacks and countermeasures for regression learning
Matthew Jagielski, Alina Oprea, Battista Biggio, Chang Liu, Cristina Nita-Rotaru, and Bo Li · 2021
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Just how toxic is data poisoning? a unified benchmark for backdoor and data poisoning attacks, 2021
Avi Schwarzschild, Micah Goldblum, Arjun Gupta, John P Dickerson, and Tom Goldstein · 2021
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Certified robustness of nearest neighbors against data poisoning and backdoor attacks
Jinyuan Jia, Yupei Liu, Xiaoyu Cao, and Neil Zhenqiang Gong · 2022
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Poison forensics: Traceback of data poisoning attacks in neural networks
Shawn Shan, Arjun Nitin Bhagoji, Haitao Zheng, and Ben Y Zhao · 2022
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Sleeper agent: Scalable hidden trigger backdoors for neural networks trained from scratch
Hossein Souri, Liam H Fowl, Rama Chellappa, Micah Goldblum, and Tom Goldstein · 2022
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A comprehensive survey on deep clustering: Taxonomy, challenges, and future directions, 2022
Sheng Zhou, Hongjia Xu, Zhuonan Zheng, Jiawei Chen, Zhao li, Jiajun Bu, Jia Wu, Xin Wang, Wenwu Zhu, and Martin Ester · 2022
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