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We tackle the problem of feature unlearning from a pre-trained image generative model: GANs and VAEs.
Gradient-based learning applied to document recognition
LeCun, Y.; Bottou, L.; Bengio, Y.; and Haffner, P. 1998 · 1998
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Distant supervision for relation extraction without labeled data
Mintz, M.; Bills, S.; Snow, R.; and Jurafsky, D. 2009 · 2009
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The right to be forgotten
Rosen, J. 2011 · 2011
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Auto-encoding variational bayes
Kingma, D. P.; and Welling, M. 2013 · 2013
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Towards making systems forget with machine unlearning
Cao, Y.; and Yang, J. 2015 · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A.; Metz, L.; and Chintala, S. 2015 · 2015
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Perceptual losses for real-time style transfer and super-resolution
Johnson, J.; Alahi, A.; and Fei-Fei, L. 2016 · 2016
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Context encoders: Feature learning by inpainting
Pathak, D.; Krahenbuhl, P.; Donahue, J.; Darrell, T.; and Efros, A. A. 2016 · 2016
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Improved techniques for training gans
Salimans, T.; Goodfellow, I.; Zaremba, W.; Cheung, V.; Radford, A.; and Chen, X. 2016 · 2016
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White, T. 2016 · 2016
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Colorful image colorization
Zhang, R.; Isola, P.; and Efros, A. A. 2016 · 2016
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Loss functions for image restoration with neural networks
Zhao, H.; Gallo, O.; Frosio, I.; and Kautz, J. 2016 · 2016
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M.; Ramsauer, H.; Unterthiner, T.; Nessler, B.; and Hochreiter, S. 2017 · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Howard, A. G.; Zhu, M.; Chen, B.; Kalenichenko, D.; Wang, W.; Weyand, T.; Andreetto, M.; and Adam, H. 2017 · 2017
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Image-to-image translation with conditional adversarial networks
Isola, P.; Zhu, J.-Y.; Zhou, T.; and Efros, A. A. 2017 · 2017
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Progressive growing of gans for improved quality, stability, and variation
Karras, T.; Aila, T.; Laine, S.; and Lehtinen, J. 2017 · 2017
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Towards deep learning models resistant to adversarial attacks
Madry, A.; Makelov, A.; Schmidt, L.; Tsipras, D.; and Vladu, A. 2017 · 2017
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Disentangled representation learning gan for pose-invariant face recognition
Tran, L.; Yin, X.; and Liu, X. 2017 · 2017
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Large-scale celebfaces attributes (celeba) dataset
Liu, Z.; Luo, P.; Wang, X.; and Tang, X. 2018 · 2018
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Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning
Castro, D. C.; Tan, J.; Kainz, B.; Konukoglu, E.; and Glocker, B. 2019 · 2019
Cited alongside, same era.
Making ai forget you: Data deletion in machine learning
Ginart, A.; Guan, M.; Valiant, G.; and Zou, J. Y. 2019 · 2019
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Ganalyze: Toward visual definitions of cognitive image properties
Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images
Child, R. 2021 · 2021
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Adaptive machine unlearning
Gupta, V.; Jung, C.; Neel, S.; Roth, A.; Sharifi-Malvajerdi, S.; and Waites, C. 2021 · 2021
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Closed-form factorization of latent semantics in gans
Shen, Y.; and Zhou, B. 2021 · 2021
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Fast yet effective machine unlearning
Tarun, A. K.; Chundawat, V. S.; Mandal, M.; and Kankanhalli, M. 2021 · 2021
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WarpedGANSpace: Finding non-linear RBF paths in GAN latent space
Tzelepis, C.; Tzimiropoulos, G.; and Patras, I. 2021 · 2021
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The geometry of deep generative image models and its applications
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Goetschalckx, L.; Andonian, A.; Oliva, A.; and Isola, P. 2019 · 2019
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A style-based generator architecture for generative adversarial networks
Karras, T.; Laine, S.; and Aila, T. 2019 · 2019
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Challenging common assumptions in the unsupervised learning of disentangled representations
Locatello, F.; Bauer, S.; Lucic, M.; Raetsch, G.; Gelly, S.; Schölkopf, B.; and Bachem, O. 2019 · 2019
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Adversarial examples: Attacks and defenses for deep learning
Yuan, X.; He, P.; Zhu, Q.; and Li, X. 2019 · 2019
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Eternal sunshine of the spotless net: Selective forgetting in deep networks
Golatkar, A.; Achille, A.; and Soatto, S. 2020 · 2020
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Generative adversarial networks
Goodfellow, I.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; and Bengio, Y. 2020 · 2020
Cited alongside, same era.
Ganspace: Discovering interpretable gan controls
Härkönen, E.; Hertzmann, A.; Lehtinen, J.; and Paris, S. 2020 · 2020
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Wang, B.; and Ponce, C. R. 2021 · 2021
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Machine unlearning: Linear filtration for logit-based classifiers
Baumhauer, T.; Schöttle, P.; and Zeppelzauer, M. 2022 · 2022
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Chundawat, V. S.; Tarun, A. K.; Mandal, M.; and Kankanhalli, M. 2022 · 2022
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Guo, T.; Guo, S.; Zhang, J.; Xu, W.; and Wang, J. 2022 · 2022
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Studiogan: A taxonomy and benchmark of gans for image synthesis
Kang, M.; Shin, J.; and Park, J. 2022 · 2022
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Data Redaction from Pre-trained GANs
Kong, Z.; and Chaudhuri, K. 2022 · 2022
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A survey of machine unlearning
Nguyen, T. T.; Huynh, T. T.; Nguyen, P. L.; Liew, A. W.-C.; Yin, H.; and Nguyen, Q. V. H. 2022 · 2022
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Few-Shot Unlearning by Model Inversion
Yoon, Y.; Nam, J.; Yun, H.; Kim, D.; and Ok, J. 2022 · 2022
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Erasing concepts from diffusion models
Gandikota, R.; Materzynska, J.; Fiotto-Kaufman, J.; and Bau, D. 2023 · 2023
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Forget-me-not: Learning to forget in text-to-image diffusion models
Zhang, E.; Wang, K.; Xu, X.; Wang, Z.; and Shi, H. 2023 · 2023
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