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We introduce DeepInversion, a new method for synthesizing images from the image distribution used to train a deep neural network.
Born again trees
L. Breiman and N. Shang · 1996
Earlier work this paper cites.
Model compression
C. Buciluǎ, R. Caruana, and A. Niculescu-Mizil · 2006
Earlier work this paper cites.
Automated flower classification over a large number of classes
M.-E. Nilsback and A. Zisserman · 2008
Earlier work this paper cites.
ImageNet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
The PASCAL visual object classes (VOC) challenge
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman · 2010
Earlier work this paper cites.
Caltech-UCSD Birds 200
P. Welinder, S. Branson, T. Mita, C. Wah, F. Schroff, S. Belongie, and P. Perona · 2010
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
K. Simonyan, A. Vedaldi, and A. Zisserman · 2013
Earlier work this paper cites.
Do deep nets really need to be deep?
J. Ba and R. Caruana · 2014
Earlier work this paper cites.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Earlier work this paper cites.
Microsoft COCO: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
Earlier work this paper cites.
Model inversion attacks that exploit confidence information and basic countermeasures
M. Fredrikson, S. Jha, and T. Ristenpart · 2015
Earlier work this paper cites.
Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. Dean · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Earlier work this paper cites.
Understanding deep image representations by inverting them
A. Mahendran and A. Vedaldi · 2015
Earlier work this paper cites.
Inceptionism: Going deeper into neural networks
A. Mordvintsev, C. Olah, and M. Tyka · 2015
Earlier work this paper cites.
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
A. Nguyen, J. Yosinski, and J. Clune · 2015
Earlier work this paper cites.
FitNets: Hints for thin deep nets
A. Romero, N. Ballas, S. E. Kahou, A. Chassang, C. Gatta, and Y. Bengio · 2015
Earlier work this paper cites.
Regression model fitting under differential privacy and model inversion attack
Y. Wang, C. Si, and X. Wu · 2015
Earlier work this paper cites.
Net2net: Accelerating learning via knowledge transfer
T. Chen, I. Goodfellow, and J. Shlens · 2016
Earlier work this paper cites.
Inverting visual representations with convolutional networks
A. Dosovitskiy and T. Brox · 2016
Earlier work this paper cites.
Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding
S. Han, H. Mao, and W. J. Dally · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
Overcoming catastrophic forgetting in neural networks
J. Kirkpatrick, R. Pascanu, N. C. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska, D. Hassabis, C. Clopath, D. Kumaran, and R. Hadsell · 2016
Earlier work this paper cites.
Unifying distillation and privileged information
D. Lopez-Paz, L. Bottou, B. Schölkopf, and V. Vapnik · 2016
Earlier work this paper cites.
Visualizing deep convolutional neural networks using natural pre-images
A. Mahendran and A. Vedaldi · 2016
Earlier work this paper cites.
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
Earlier work this paper cites.
Improved techniques for training GANs
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
Cited alongside, same era.
Improved training of Wasserstein GANs
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville · 2017
Cited alongside, same era.
Densely connected convolutional networks
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger · 2017
Cited alongside, same era.
Pruning filters for efficient ConvNets
H. Li, A. Kadav, I. Durdanovic, H. Samet, and H. P. Graf · 2017
Cited alongside, same era.
Learning without forgetting
Z. Li and D. Hoiem · 2017
Cited alongside, same era.
Learning efficient convolutional networks through network slimming
Z. Liu, J. Li, Z. Shen, G. Huang, S. Yan, and C. Zhang · 2017
Cited alongside, same era.
Spectral normalization for generative adversarial networks
T. Miyato, T. Kataoka, M. Koyama, and Y. Yoshida · 2018
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Variational continual learning
C. V. Nguyen, Y. Li, T. D. Bui, and R. E. Turner · 2018
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Model compression via distillation and quantization
A. Polino, R. Pascanu, and D. Alistarh · 2018
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How good is my GAN?
K. Shmelkov, C. Schmid, and K. Alahari · 2018
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Training shallow and thin networks for acceleration via knowledge distillation with conditional adversarial networks
Z. Xu, Y.-C. Hsu, and J. Huang · 2018
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Rethinking the smaller-norm-less-informative assumption in channel pruning of convolution layers
J. Ye, X. Lu, Z. Lin, and J. Z. Wang · 2018
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R. G. Lopes, S. Fenu, and T. Starner · 2017
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ThiNet: A filter level pruning method for deep neural network compression
J.-H. Luo, J. Wu, and W. Lin · 2017
Cited alongside, same era.
Pruning convolutional neural networks for resource efficient transfer learning
P. Molchanov, S. Tyree, T. Karras, T. Aila, and J. Kautz · 2017
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Plug & play generative networks: Conditional iterative generation of images in latent space
A. Nguyen, J. Clune, Y. Bengio, A. Dosovitskiy, and J. Yosinski · 2017
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iCaRL: Incremental classifier and representation learning
S.-A. Rebuffi, A. Kolesnikov, G. Sperl, and C. H. Lampert · 2017
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Continual learning with deep generative replay
H. Shin, J. K. Lee, J. Kim, and J. Kim · 2017
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NISP: Pruning networks using neuron importance score propagation
R. Yu, A. Li, C.-F. Chen, J.-H. Lai, V. I. Morariu, X. Han, M. Gao, C.-Y. Lin, and L. S. Davis · 2018
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Variational information distillation for knowledge transfer
S. Ahn, S. X. Hu, A. Damianou, N. D. Lawrence, and Z. Dai · 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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Large scale GAN training for high fidelity natural image synthesis
A. Brock, J. Donahue, and K. Simonyan · 2019
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Data-free learning of student networks
H. Chen, Y. Wang, C. Xu, Z. Yang, C. Liu, B. Shi, C. Xu, C. Xu, and Q. Tian · 2019
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ChamNet: Towards efficient network design through platform-aware model adaptation
X. Dai, P. Zhang, B. Wu, H. Yin, F. Sun, Y. Wang, M. Dukhan, Y. Hu, Y. Wu, Y. Jia, P. Vajda, M. Uyttendaele, and N. K. Jha · 2019
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Model inversion attacks against collaborative inference
Z. He, T. Zhang, and R. B. Lee · 2019
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Overcoming catastrophic forgetting for continual learning via model adaptation
W. Hu, Z. Lin, B. Liu, C. Tao, J. Tao, J. Ma, D. Zhao, and R. Yan · 2019
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Structured knowledge distillation for semantic segmentation
Y. Liu, K. Chen, C. Liu, Z. Qin, Z. Luo, and J. Wang · 2019
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Importance estimation for neural network pruning
P. Molchanov, A. Mallya, S. Tyree, I. Frosio, and J. Kautz · 2019
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ResNet50v1.5 training
NVIDIA · 2019
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Relational knowledge distillation
W. Park, D. Kim, Y. Lu, and M. Cho · 2019
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Refine and distill: Exploiting cycle-inconsistency and knowledge distillation for unsupervised monocular depth estimation
A. Pilzer, S. Lathuiliere, N. Sebe, and E. Ricci · 2019
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HAQ: Hardware-aware automated quantization with mixed precision
K. Wang, Z. Liu, Y. Lin, J. Lin, and S. Han · 2019
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FBNet: Hardware-aware efficient ConvNet design via differentiable neural architecture search
B. Wu, X. Dai, P. Zhang, Y. Wang, F. Sun, Y. Wu, Y. Tian, P. Vajda, Y. Jia, and K. Keutzer · 2019
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Adversarial neural network inversion via auxiliary knowledge alignment
Z. Yang, E.-C. Chang, and Z. Liang · 2019
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Hardware-guided symbiotic training for compact, accurate, yet execution-efficient LSTM
H. Yin, G. Chen, Y. Li, S. Che, W. Zhang, and N. K. Jha · 2019
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Self-attention generative adversarial networks
H. Zhang, I. Goodfellow, D. Metaxas, and A. Odena · 2019
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ZeroQ: A novel zero shot quantization framework
Y. Cai, Z. Yao, Z. Dong, A. Gholami, M. W. Mahoney, and K. Keutzer · 2020
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The knowledge within: Methods for data-free model compression
M. Haroush, I. Hubara, E. Hoffer, and D. Soudry · 2020
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