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We study the task of replicating the functionality of black-box neural models, for which we only know the output class probabilities provided for a set of input images.
Gradient-based learning applied to document recognition
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A. Krizhevsky · 2009
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ImageNet Classification with Deep Convolutional Neural Networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Do deep nets really need to be deep?
J. Ba and R. Caruana · 2014
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Distilling the Knowledge in a Neural Network
G. Hinton, O. Vinyals, and J. Dean · 2014
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Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2014
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Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan and A. Zisserman · 2014
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Adam: A Method for Stochastic Optimization
D. P. Kingma and J. Ba · 2015
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Deep learning
Y. LeCun, Y. Bengio, and G. Hinton · 2015
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ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
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Deep Residual Learning for Image Recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Unifying distillation and privileged information
D. Lopez-Paz, L. Bottou, B. Schölkopf, and V. Vapnik · 2016
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Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
A. Nguyen, A. Dosovitskiy, J. Yosinski, T. Brox, and J. Clune · 2016
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Stealing Machine Learning Models via Prediction APIs
F. Tramèr, F. Zhang, A. Juels, M. K. Reiter, and T. Ristenpart · 2016
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Practical Black-Box Attacks against Machine Learning
N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, and A. Swami · 2017
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Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
H. Xiao, K. Rasul, and R. Vollgraf · 2017
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Learning from multiple teacher networks
S. You, C. Xu, C. Xu, and D. Tao · 2017
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Machine Learning with AWS: Explore the Power of Cloud Services for Your Machine Learning and Artificial Intelligence Projects
J. Jackovich and R. Richards · 2018
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Progressive Growing of GANs for Improved Quality, Stability, and Variation
Few shot network compression via cross distillation
H. Bai, J. Wu, I. King, and M. Lyu · 2019
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Dream distillation: A data-independent model compression framework
K. Bhardwaj, N. Suda, and R. Marculescu · 2019
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2019
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ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
R. Geirhos, P. Rubisch, C. Michaelis, M. Bethge, F. A. Wichmann, and W. Brendel · 2019
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The Knowledge Within: Methods for Data-Free Model Compression
M. Haroush, I. Hubara, E. Hoffer, and D. Soudry · 2019
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Disconnected Manifold Learning for Generative Adversarial Networks
M. Khayatkhoei, M. K. Singh, and A. Elgammal · 2018
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The State of Machine Learning Adoption in the Enterprise
B. Lorica and P. Nathan · 2018
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cGANs with Projection Discriminator
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Spectral Normalization for Generative Adversarial Networks
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Towards Reverse-Engineering Black-Box Neural Networks
S. J. Oh, M. Augustin, B. Schiele, and M. Fritz · 2018
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Stealing Hyperparameters in Machine Learning
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PRADA: Protecting against DNN model stealing attacks
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Zero-shot Knowledge Transfer via Adversarial Belief Matching
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Zero-Shot Knowledge Distillation in Deep Networks
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Knockoff Nets: Stealing Functionality of Black-Box Models
T. Orekondy, B. Schiele, and M. Fritz · 2019
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Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion
H. Yin, P. Molchanov, Z. Li, J. M. Alvarez, A. Mallya, D. Hoiem, N. K. Jha, and J. Kautz · 2019
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DeGAN: Data-Enriching GAN for Retrieving Representative Samples from a Trained Classifier
S. Addepalli, G. K. Nayak, A. Chakraborty, and R. V. Babu · 2020
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