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Convolutional neural networks memorize part of their training data, which is why strategies such as data augmentation and drop-out are employed to mitigate overfitting.
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Calibrating noise to sensitivity in private data analysis
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Building the gist of a scene: The role of global image features in recognition
Aude Oliva and Antonio Torralba · 2006
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80 million tiny images: A large data set for nonparametric object and scene recognition
Antonio Torralba, Rob Fergus, and William T Freeman · 2008
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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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Evaluation of gist descriptors for web-scale image search
Matthijs Douze, Hervé Jégou, Harsimrat Sandhawalia, Laurent Amsaleg, and Cordelia Schmid · 2009
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Learning in a large function space: Privacy-preserving mechanisms for SVM learning
Benjamin IP Rubinstein, Peter L Bartlett, Ling Huang, and Nina Taft · 2009
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Unbiased look at dataset bias
Antonio Torralba and Alexei A Efros · 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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Challenging differential privacy: the case of non-interactive mechanisms
Raghavendran Balu, Teddy Furon, and Sébastien Gambs · 2014
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Security Evaluation of Support Vector Machines in Adversarial Environments
Battista Biggio, Igino Corona, Blaine Nelson, Benjamin I. P. Rubinstein, Davide Maiorca, Giorgio Fumera, Giorgio Giacinto, and Fabio Roli · 2014
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Discriminative unsupervised feature learning with convolutional neural networks
Alexey Dosovitskiy, Jost Tobias Springenberg, Martin Riedmiller, and Thomas Brox · 2014
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Transformation pursuit for image classification
Mattis Paulin, Jérôme Revaud, Zaid Harchaoui, Florent Perronnin, and Cordelia Schmid · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Visualizing and understanding convolutional networks
On the protection of private information in machine learning systems: Two recent approches
Martín Abadi, Ulfar Erlingsson, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Nicolas Papernot, Kunal Talwar, and Li Zhang · 2017
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ADA: A game-theoretic perspective on data augmentation for object detection
S. Behpour, K. Kitani, and B. Ziebart · 2017
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Unsupervised learning by predicting noise
Piotr Bojanowski and Armand Joulin · 2017
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Cut, paste and learn: Surprisingly easy synthesis for instance detection
Debidatta Dwibedi, Ishan Misra, and Martial Hebert · 2017
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Low-shot visual recognition by shrinking and hallucinating features
Bharath Hariharan and Ross Girshick · 2017
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Logan: evaluating privacy leakage of generative models using generative adversarial networks
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Matthew D Zeiler and Rob Fergus · 2014
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Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers
Giuseppe Ateniese, Luigi V Mancini, Angelo Spognardi, Antonio Villani, Domenico Vitali, and Giovanni Felici · 2015
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Understanding image representations by measuring their equivariance and equivalence
Karel Lenc and Andrea Vedaldi · 2015
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Understanding deep image representations by inverting them
Aravindh Mahendran and Andrea Vedaldi · 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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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Algorithmic stability for adaptive data analysis
Raef Bassily, Kobbi Nissim, Adam Smith, Thomas Steinke, Uri Stemmer, and Jonathan Ullman · 2016
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Jamie Hayes, Luca Melis, George Danezis, and Emiliano De Cristofaro · 2017
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Mask R-CNN
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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Memory vectors for similarity search in high-dimensional spaces
Ahmet Iscen, Teddy Furon, Vincent Gripon, Michael Rabbat, and Hervé Jégou · 2017
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Billion-scale similarity search with gpus
Jeff Johnson, Matthijs Douze, and Hervé Jégou · 2017
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The case for learned index structures
Tim Kraska, Alex Beutel, Ed H Chi, Jeffrey Dean, and Neoklis Polyzotis · 2017
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A closer look at memorization in deep networks
David Krueger, Nicolas Ballas, Stanislaw Jastrzebski, Devansh Arpit, Maxinder S. Kanwal, Tegan Maharaj, Emmanuel Bengio, Asja Fischer, Aaron Courville, Simon Lacoste-Julien, and Yoshua Bengio · 2017
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Convolutional patch representations for image retrieval: an unsupervised approach
Mattis Paulin, Julien Mairal, Matthijs Douze, Zaid Harchaoui, Florent Perronnin, and Cordelia Schmid · 2017
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A deeper look at dataset bias
Tatiana Tommasi, Novi Patricia, Barbara Caputo, and Tinne Tuytelaars · 2017
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Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2017
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The secret sharer: Measuring unintended neural network memorization & extracting secrets
Nicholas Carlini, Chang Liu, Jernej Kos, Úlfar Erlingsson, and Dawn Song · 2018
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Understanding membership inferences on well-generalized learning models
Yunhui Long, Vincent Bindschaedler, Lei Wang, Diyue Bu, Xiaofeng Wang, Haixu Tang, Carl A Gunter, and Kai Chen · 2018
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Privacy risk in machine learning: Analyzing the connection to overfitting
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha · 2018
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