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Self-supervised learning is an emerging machine learning paradigm.
Visualizing Data using t-SNE
Laurens van der Maaten and Geoffrey Hinton · 2008
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An Analysis of Single-Layer Networks in Unsupervised Feature Learning
Adam Coates, Andrew Y. Ng, and Honglak Lee · 2011
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The German Traffic Sign Recognition Benchmark: A Multi-class Classification Competition
Johannes Stallkamp, Marc Schlipsing, Jan Salmen, and Christian Igel · 2011
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Distributions of Angles in Random Packing on Spheres
T. Tony Cai, Jianqing Fan, and Tiefeng Jiang · 2013
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Explaining and Harnessing Adversarial Examples
Ian Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba · 2015
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ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Adversarial Examples in the Physical World
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
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Delving into Transferable Adversarial Examples and Black-box Attacks
Yanpei Liu, Xinyun Chen, Chang Liu, and Dawn Song · 2016
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Stealing Machine Learning Models via Prediction APIs
Florian Tramèr, Fan Zhang, Ari Juels, Michael K. Reiter, and Thomas Ristenpart · 2016
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Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods
Nicholas Carlini and David Wagner · 2017
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Adversarial Frontier Stitching for Remote Neural Network Watermarking
Erwan Le Merrer, Patrick Perez, and Gilles Trédan · 2017
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Practical Black-Box Attacks Against Machine Learning
Nicolas Papernot, Patrick D. McDaniel, Ian Goodfellow, Somesh Jha, Z. Berkay Celik, and Ananthram Swami · 2017
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Membership Inference Attacks Against Machine Learning Models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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Embedding Watermarks into Deep Neural Networks
Yusuke Uchida, Yuki Nagai, Shigeyuki Sakazawa, and Shin’ichi Satoh · 2017
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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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Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring
Yossi Adi, Carsten Baum, Moustapha Cisse, Benny Pinkas, and Joseph Keshet · 2018
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Model Extraction and Active Learning
Varun Chandrasekaran, Kamalika Chaudhuri, Irene Giacomelli, Somesh Jha, and Songbai Yan · 2018
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Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks
Kang Liu, Brendan Dolan-Gavitt, and Siddharth Garg · 2018
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DeepSigns: A Generic Watermarking Framework for IP Protection of Deep Learning Models
Bita Darvish Rouhani, Huili Chen, and Farinaz Koushanfar · 2018
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Group Normalization
Yuxin Wu and Kaiming He · 2018
Cited alongside, same era.
Protecting Intellectual Property of Deep Neural Networks with Watermarking
Jialong Zhang, Zhongshu Gu, Jiyong Jang, Hui Wu, Marc Ph. Stoecklin, Heqing Huang, and Ian Molloy · 2018
Cited alongside, same era.
To Prune, or Not to Prune: Exploring the Efficacy of Pruning for Model Compression
Michael Zhu and Suyog Gupta · 2018
Cited alongside, same era.
Adversarial Model Extraction on Graph Neural Networks
David DeFazio and Arti Ramesh · 2019
Cited alongside, same era.
How to Prove Your Model Belongs to You: A Blind-Watermark based Framework to Protect Intellectual Property of DNN
Zheng Li, Chengyu Hu, Yang Zhang, and Shanqing Guo · 2019
Cited alongside, same era.
IPGuard: Protecting Intellectual Property of Deep Neural Networks via Fingerprinting the Classification Boundary
Xiaoyu Cao, Jinyuan Jia, and Neil Zhenqiang Gong · 2021
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BadNL: Backdoor Attacks Against NLP Models with Semantic-preserving Improvements
Xiaoyi Chen, Ahmed Salem, Michael Backes, Shiqing Ma, Qingni Shen, Zhonghai Wu, and Yang Zhang · 2021
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Anti-Distillation Backdoor Attacks: Backdoors Can Really Survive in Knowledge Distillation
Yunjie Ge, Qian Wang, Baolin Zheng, Xinlu Zhuang, Qi Li, Chao Shen, and Cong Wang · 2021
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DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations
John M. Giorgi, Osvald Nitski, Bo Wang, and Gary D. Bader · 2021
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Node-Level Membership Inference Attacks Against Graph Neural Networks
Xinlei He, Rui Wen, Yixin Wu, Michael Backes, Yun Shen, and Yang Zhang · 2021
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Knockoff Nets: Stealing Functionality of Black-Box Models
Tribhuvanesh Orekondy, Bernt Schiele, and Mario Fritz · 2019
Cited alongside, same era.
ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models
Ahmed Salem, Yang Zhang, Mathias Humbert, Pascal Berrang, Mario Fritz, and Michael Backes · 2019
Cited alongside, same era.
Latent Backdoor Attacks on Deep Neural Networks
Yuanshun Yao, Huiying Li, Haitao Zheng, and Ben Y. Zhao · 2019
Cited alongside, same era.
Language Models are Few-Shot Learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
Cited alongside, same era.
Exploring Connections Between Active Learning and Model Extraction
Varun Chandrasekaran, Kamalika Chaudhuri, Irene Giacomelli, Somesh Jha, and Songbai Yan · 2020
Cited alongside, same era.
A Simple Framework for Contrastive Learning of Visual Representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton · 2020
Cited alongside, same era.
Improved Baselines with Momentum Contrastive Learning
Xinlei Chen, Haoqi Fan, Ross B. Girshick, and Kaiming He · 2020
Cited alongside, same era.
Quantifying and Mitigating Privacy Risks of Contrastive Learning
Xinlei He and Yang Zhang · 2021
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Entangled Watermarks as a Defense against Model Extraction
Hengrui Jia, Christopher A. Choquette-Choo, Varun Chandrasekaran, and Nicolas Papernot · 2021
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10 Security and Privacy Problems in Self-Supervised Learning
Jinyuan Jia, Hongbin Liu, and Neil Zhenqiang Gong · 2021
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Membership Leakage in Label-Only Exposures
Zheng Li and Yang Zhang · 2021
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EncoderMI: Membership Inference against Pre-trained Encoders in Contrastive Learning
Hongbin Liu, Jinyuan Jia, Wenjie Qu, and Neil Zhenqiang Gong · 2021
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Learning Transferable Visual Models From Natural Language Supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever · 2021
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Systematic Evaluation of Privacy Risks of Machine Learning Models
Liwei Song and Prateek Mittal · 2021
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Copy, Right? A Testing Framework for Copyright Protection of Deep Learning Models
Jialuo Chen, Jingyi Wang, Tinglan Peng, Youcheng Sun, Peng Cheng, Shouling Ji, Xingjun Ma, Bo Li, and Dawn Song · 2022
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Membership-Doctor: Comprehensive Assessment of Membership Inference Against Machine Learning Models
Xinlei He, Zheng Li, Weilin Xu, Cory Cornelius, and Yang Zhang · 2022
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Semi-Leak: Membership Inference Attacks Against Semi-supervised Learning
Xinlei He, Hongbin Liu, Neil Zhenqiang Gong, and Yang Zhang · 2022
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BadEncoder: Backdoor Attacks to Pre-trained Encoders in Self-Supervised Learning
Jinyuan Jia, Yupei Liu, and Neil Zhenqiang Gong · 2022
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Auditing Membership Leakages of Multi-Exit Networks
Zheng Li, Yiyong Liu, Xinlei He, Ning Yu, Michael Backes, and Yang Zhang · 2022
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SoK: How Robust is Image Classification Deep Neural Network Watermarking?
Nils Lukas, Edward Jiang, Xinda Li, and Florian Kerschbaum · 2022
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Model Stealing Attacks Against Inductive Graph Neural Networks
Yun Shen, Xinlei He, Yufei Han, and Yang Zhang · 2022
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