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Recent breakthroughs in generative modeling have sparked interest in practical single-model attribution.
Regression shrinkage and selection via the lasso
Tibshirani, R · 1996
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Atomic decomposition by basis pursuit
Chen, S. S., Donoho, D. L., and Saunders, M. A · 2001
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Support vector data description
Tax, D. M. J. and Duin, R. P. W · 2004
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A simple proof of the restricted isometry property for random matrices
Baraniuk, R., Davenport, M. A., DeVore, R. A., and Wakin, M. B · 2008
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The restricted isometry property and its implications for compressed sensing
Candès, E. J · 2008
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A fast iterative shrinkage-thresholding algorithm for linear inverse problems
Beck, A. and Teboulle, M · 2009
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Modeling wine preferences by data mining from physicochemical properties
Cortez, P., Cerdeira, A., Almeida, F., Matos, T., and Reis, J · 2009
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Toeplitz compressed sensing matrices with applications to sparse channel estimation
Haupt, J., Bajwa, W. U., Raz, G., and Nowak, R · 2010
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Compressed Sensing: Theory and Applications
Eldar, Y. C. and Kutyniok, G · 2012
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Linear Algebra
Hefferon, J · 2012
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BCDR: A breast cancer digital repository
Lopez, M. A. G., Posada, N., Moura, D. C., Pollán, R. R., Jose, M. G.-V., Valiente, F. S., Ortega, C. S., del Solar, M. R., Herrero, G. D., IsabelM., A., Ramos, P., Loureiro, J., Fernandes, T. C., and de Araújo, B. M. F · 2012
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Certifying the restricted isometry property is hard
Bandeira, A. S., Dobriban, E., Mixon, D. G., and Sawin, W. F · 2013
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The restricted isometry property for random convolutions
Krahmer, F., Mendelson, S., and Rauhut, H · 2013
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The lasso problem and uniqueness
Tibshirani, R. J · 2013
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Microsoft COCO: Common objects in context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C. L · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
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LSUN: Construction of a large-scale image dataset using deep learning with humans in the loop
Yu, F., Zhang, Y., Song, S., Seff, A., and Xiao, J · 2015
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The Synthetic data vault
Patki, N., Wedge, R., and Veeramachaneni, K · 2016
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S · 2016
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Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
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Energy-based generative adversarial network
Zhao, J., Mathieu, M., and LeCun, Y · 2016
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Improved training of Wasserstein GANs
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. C · 2017
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Least squares generative adversarial networks
Mao, X., Li, Q., Xie, H., Lau, R. Y., Wang, Z., and Paul Smolley, S · 2017
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Turning your weakness into a strength: Watermarking deep neural networks by backdooring
Adi, Y., Baum, C., Cisse, M., Pinkas, B., and Keshet, J · 2018
Cited alongside, same era.
How faking videos became easy and why that’s so scary
Howcroft, E · 2018
Cited alongside, same era.
Deep one-class classification
Ruff, L., Vandermeulen, R., Goernitz, N., Deecke, L., Siddiqui, S. A., Binder, A., Müller, E., and Kloft, M · 2018
Cited alongside, same era.
Source generator attribution via inversion
Albright, M. and McCloskey, S · 2019
Cited alongside, same era.
A style-based generator architecture for generative adversarial networks
Karras, T., Laine, S., and Aila, T · 2019
Cited alongside, same era.
Inverting deep generative models, one layer at a time
Lei, Q., Jalal, A., Dhillon, I. S., and Dimakis, A. G · 2019
Cited alongside, same era.
Zhang, B., Zhou, J. P., Shumailov, I., and Papernot, N · 2021
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Did you use my GAN to generate fake? Post-hoc attribution of GAN generated images via latent recovery
Hirofumi, S., Fukuchi, K., Akimoto, Y., and Sakuma, J · 2022
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More real than real: A study on human visual perception of synthetic faces [applications corner]
Lago, F., Pasquini, C., Böhme, R., Dumont, H., Goffaux, V., and Boato, G · 2022
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Fundamentals of High-Dimensional Statistics: With Exercises and R Labs
Lederer, J · 2022
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DeepPhish: Understanding user trust towards artificially generated profiles in online social networks
Mink, J., Luo, L., Barbosa, N. M., Figueira, O., Wang, Y., and Wang, G · 2022
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Modeling tabular data using conditional GAN
Xu, L., Skoularidou, M., Cuesta-Infante, A., and Veeramachaneni, K · 2019
Cited alongside, same era.
Scalable fine-grained generated image classification based on deep metric learning
Xuan, X., Peng, B., Wang, W., and Dong, J · 2019
Cited alongside, same era.
Attributing fake images to GANs: Learning and analyzing GAN fingerprints
Yu, N., Davis, L. S., and Fritz, M · 2019
Cited alongside, same era.
DCGANs for realistic breast mass augmentation in x-ray mammography
Alyafi, B., Diaz, O., and Marti, R · 2020
Cited alongside, same era.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D · 2020
Cited alongside, same era.
Leveraging frequency analysis for deep fake image recognition
Frank, J., Eisenhofer, T., Schönherr, L., Fischer, A., Kolossa, D., and Holz, T · 2020
Cited alongside, same era.
Deep learning for deepfakes creation and detection: A survey
Nguyen, T. T., Nguyen, Q. V. H., Nguyen, D. T., Nguyen, D. T., Huynh-The, T., Nahavandi, S., Nguyen, T. T., Pham, Q.-V., and Nguyen, C. M · 2022
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AI-synthesized faces are indistinguishable from real faces and more trustworthy
Nightingale, S. J. and Farid, H · 2022
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Hierarchical text-conditional image generation with CLIP latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M · 2022
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High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E., Ghasemipour, S. K. S., Ayan, B. K., Mahdavi, S. S., Lopes, R. G., Salimans, T., Ho, J., Fleet, D. J., and Norouzi, M · 2022
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StyleGAN-XL: Scaling StyleGAN to large diverse datasets
Sauer, A., Schwarz, K., and Geiger, A · 2022
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Sharing generative models instead of private data: A simulation study on mammography patch classification
Szafranowska, Z., Osuala, R., Breier, B., Kushibar, K., Lekadir, K., and Diaz, O · 2022
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Machine learning for medical imaging: Methodological failures and recommendations for the future
Varoquaux, G. and Cheplygina, V · 2022
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StyleNAT: Giving each head a new perspective
Walton, S., Hassani, A., Xu, X., Wang, Z., and Shi, H · 2022
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Responsible disclosure of generative models using scalable fingerprinting
Yu, N., Skripniuk, V., Chen, D., Davis, L. S., and Fritz, M · 2022
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StyleSwin: Transformer-based GAN for high-resolution image generation
Zhang, B., Gu, S., Zhang, B., Bao, J., Chen, D., Wen, F., Wang, Y., and Guo, B · 2022
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Statistical guarantees for sparse deep learning
Lederer, J · 2023
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Attributing image generative models using latent fingerprints
Nie, G., Kim, C., Yang, Y., and Ren, Y · 2023
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medigan: A Python library of pretrained generative models for medical image synthesis
Osuala, R., Skorupko, G., Lazrak, N., Garrucho, L., García, E., Joshi, S., Jouide, S., Rutherford, M., Prior, F., Kushibar, K., et al · 2023
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Could a robot ever recreate the aura of a Leonardo Da Vinci masterpiece? It’s already happening
Rea, N · 2023
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How generative AI is boosting the spread of disinformation and propaganda
Ryan-Mosley, T · 2023
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Tree-ring watermarks: Fingerprints for diffusion images that are invisible and robust
Wen, Y., Kirchenbauer, J., Geiping, J., and Goldstein, T · 2023
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Three ways we can fight deepfake porn
Heikkilä, M · 2024
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