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Large pre-trained generative models are known to occasionally output undesirable samples, which undermines their trustworthiness.
Learning multiple layers of features from tiny images
A. Krizhevsky, G. Hinton, et al · 2009
Earlier work this paper cites.
Mnist handwritten digit database
Y. LeCun, C. Cortes, and C. Burges · 2010
Earlier work this paper cites.
Estimating divergence functionals and the likelihood ratio by convex risk minimization
X. Nguyen, M. J. Wainwright, and M. I. Jordan · 2010
Earlier work this paper cites.
An analysis of single-layer networks in unsupervised feature learning
A. Coates, A. Ng, and H. Lee · 2011
Earlier work this paper cites.
Towards making systems forget with machine unlearning
Y. Cao and J. Yang · 2015
Earlier work this paper cites.
Deep learning face attributes in the wild
Z. Liu, P. Luo, X. Wang, and X. Tang · 2015
Earlier work this paper cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2015
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.
f-gan: Training generative neural samplers using variational divergence minimization
S. Nowozin, B. Cseke, and R. Tomioka · 2016
Earlier work this paper cites.
Deconvolution and checkerboard artifacts
A. Odena, V. Dumoulin, and C. Olah · 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
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
Earlier work this paper cites.
11 adversarial perturbations of deep neural networks
D. Warde-Farley and I. Goodfellow · 2016
Earlier work this paper cites.
A. Aitken, C. Ledig, L. Theis, J. Caballero, Z. Wang, and W. Shi · 2017
Earlier work this paper cites.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter · 2017
Earlier work this paper cites.
Image-to-image translation with conditional adversarial networks
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros · 2017
Earlier work this paper cites.
Overcoming catastrophic forgetting in neural networks
J. Kirkpatrick, R. Pascanu, N. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska, et al · 2017
Earlier work this paper cites.
C. Donahue, J. McAuley, and M. Puckette · 2018
Earlier work this paper cites.
The lottery ticket hypothesis: Finding sparse, trainable neural networks
J. Frankle and M. Carbin · 2018
Earlier work this paper cites.
Actively avoiding nonsense in generative models
S. Hanneke, A. T. Kalai, G. Kamath, and C. Tzamos · 2018
Earlier work this paper cites.
Towards deep learning models resistant to adversarial attacks
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2018
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Detecting offensive language in tweets using deep learning
G. K. Pitsilis, H. Ramampiaro, and H. Langseth · 2018
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Mocogan: Decomposing motion and content for video generation
S. Tulyakov, M.-Y. Liu, X. Yang, and J. Kautz · 2018
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Training language gans from scratch
C. de Masson d’Autume, S. Mohamed, M. Rosca, and J. Rae · 2019
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Certified data removal from machine learning models
C. Guo, T. Goldstein, A. Hannun, and L. Van Der Maaten · 2019
Cited alongside, same era.
Reducing artifacts in gan audio synthesis
N. Thiem, M. Orescanin, and J. B. Michael · 2020
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Cnn-generated images are surprisingly easy to spot… for now
S.-Y. Wang, O. Wang, R. Zhang, A. Owens, and A. A. Efros · 2020
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On leveraging pretrained gans for generation with limited data
M. Zhao, Y. Cong, and L. Carin · 2020
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In-domain gan inversion for real image editing
J. Zhu, Y. Shen, D. Zhao, and B. Zhou · 2020
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Persistent anti-muslim bias in large language models
A. Abid, M. Farooqi, and J. Zou · 2021
Later among the works it cites.
Machine unlearning
L. Bourtoule, V. Chandrasekaran, C. A. Choquette-Choo, H. Jia, A. Travers, B. Zhang, D. Lie, and N. Papernot · 2021
Later among the works it cites.
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X. Zhang, S. Karaman, and S.-F. Chang · 2019
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Teaching a gan what not to learn
S. Asokan and C. Seelamantula · 2020
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Rewriting a deep generative model
D. Bau, S. Liu, T. Wang, J.-Y. Zhu, and A. Torralba · 2020
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Pytorch-playground
A. Chen · 2020
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Ganspace: Discovering interpretable gan controls
E. Härkönen, A. Hertzmann, J. Lehtinen, and S. Paris · 2020
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Analyzing and improving the image quality of stylegan
T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila · 2020
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Why normalizing flows fail to detect out-of-distribution data
P. Kirichenko, P. Izmailov, and A. G. Wilson · 2020
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Mathematica, Version 13.0.0
W. R. Inc · 2021
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Approximate data deletion from machine learning models
Z. Izzo, M. A. Smart, K. Chaudhuri, and J. Zou · 2021
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Blur, noise, and compression robust generative adversarial networks
T. Kaneko and T. Harada · 2021
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Alias-free generative adversarial networks
T. Karras, M. Aittala, S. Laine, E. Härkönen, J. Hellsten, J. Lehtinen, and T. Aila · 2021
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Descent-to-delete: Gradient-based methods for machine unlearning
S. Neel, A. Roth, and S. Sharifi-Malvajerdi · 2021
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On the frequency bias of generative models
K. Schwarz, Y. Liao, and A. Geiger · 2021
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Remember what you want to forget: Algorithms for machine unlearning
A. Sekhari, J. Acharya, G. Kamath, and A. T. Suresh · 2021
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Knowledge evolution in neural networks
A. Taha, A. Shrivastava, and L. S. Davis · 2021
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Machine unlearning via algorithmic stability
E. Ullah, T. Mai, A. Rao, R. A. Rossi, and R. Arora · 2021
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Unsupervised 3d shape completion through gan inversion
J. Zhang, X. Chen, Z. Cai, L. Pan, H. Zhao, S. Yi, C. K. Yeo, B. Dai, and C. C. Loy · 2021
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Fortuitous forgetting in connectionist networks
H. Zhou, A. Vani, H. Larochelle, and A. Courville · 2021
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Fairstyle: Debiasing stylegan2 with style channel manipulations
C. Karakas, A. Dirik, E. Yalcinkaya, and P. Yanardag · 2022
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Red teaming language models with language models
E. Perez, S. Huang, F. Song, T. Cai, R. Ring, J. Aslanides, A. Glaese, N. McAleese, and G. Irving · 2022
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