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Recently, Diffusion Models (DMs) boost a wave in AI for Art yet raise new copyright concerns, where infringers benefit from using unauthorized paintings to train DMs to generate novel paintings in a similar style.
Learning Internal Representations by Error Propagation
Rumelhart, D. E., Hinton, G. E., and Williams, R. J · 1985
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Reconstructing the Fair Use Doctrine
Fisher III, W. W · 1987
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Copyright for Visual Art in the Digital Age: A Modern Adventure in Wonderland
Sullivan, J. E · 1996
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Denoising Diffusion Implicit Models
Song, J., Meng, C., and Ermon, S · 2010
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Score-Based Generative Modeling Through Stochastic Differential Equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2011
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Nice: Non-Linear Independent Components Estimation
Dinh, L., Krueger, D., and Bengio, Y · 2014
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Auto-Encoding Variational Bayes
Kingma, D. P. and Welling, M · 2014
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Conditional Generative Adversarial Nets
Mirza, M. and Osindero, S · 2014
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Notes on Kullback-Leibler Divergence and Likelihood
Shlens, J · 2014
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Deep Unsupervised Learning Using Nonequilibrium Thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
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LSUN: Construction of a Large-Scale Image Dataset using Deep Learning with Humans in the Loop
Yu, F., Seff, A., Zhang, Y., Song, S., Funkhouser, T., and Xiao, J · 2015
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Density Estimation Using Real NVP
Dinh, L., Sohl-Dickstein, J., and Bengio, S · 2016
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Towards Conceptual Compression
Gregor, K., Besse, F., Jimenez Rezende, D., Danihelka, I., and Wierstra, D · 2016
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Painter by Numbers, WikiArt
Nichol, K · 2016
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Transferability in Machine Learning: From Phenomena to Black-Box Attacks Using Adversarial Samples
Papernot, N., McDaniel, P., and Goodfellow, I · 2016
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Towards Evaluating the Robustness of Neural Networks
Carlini, N. and Wagner, D · 2017
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Countering Adversarial Images Using Input Transformations
Guo, C., Rana, M., Cisse, M., and Van Der Maaten, L · 2017
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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
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Beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework
Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., and Lerchner, A · 2017
Earlier work this paper cites.
Adversarial Examples for Evaluating Reading Comprehension Systems
Jia, R. and Liang, P · 2017
Earlier work this paper cites.
Tactics of Adversarial Attack on Deep Reinforcement Learning Agents
Lin, Y.-C., Hong, Z.-W., Liao, Y.-H., Shih, M.-L., Liu, M.-Y., and Sun, M · 2017
Cited alongside, same era.
Inception-V3 for Flower Classification
Xia, X., Xu, C., and Nan, B · 2017
Cited alongside, same era.
Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks
Zhu, J.-Y., Park, T., Isola, P., and Efros, A. A · 2017
Cited alongside, same era.
Reclaiming Fair Use: How to Put Balance Back in Copyright
Aufderheide, P. and Jaszi, P · 2018
Cited alongside, same era.
Large Scale GAN Training for High Fidelity Natural Image Synthesis
Brock, A., Donahue, J., and Simonyan, K · 2018
Cited alongside, same era.
Diffusion Models Beat GANs on Image Synthesis
Dhariwal, P. and Nichol, A · 2021
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Learning Transferable Visual Models from Natural Language Supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
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Laion-400m: Open Dataset of Clip-Filtered 400 Million Image-Text Pairs
Schuhmann, C., Vencu, R., Beaumont, R., Kaczmarczyk, R., Mullis, C., Katta, A., Coombes, T., Jitsev, J., and Komatsuzaki, A · 2021
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Invasive Diffusion: How One Unwilling Illustrator Found Herself Turned Into an AI Model
Baio, A · 2022
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Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models
Bao, F., Li, C., Zhu, J., and Zhang, B · 2022
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Dai, H., Li, H., Tian, T., Huang, X., Wang, L., Zhu, J., and Song, L · 2018
Cited alongside, same era.
Shield: Fast, Practical Defense and Vaccination for Deep Learning Using JPEG Compression
Das, N., Shanbhogue, M., Chen, S.-T., Hohman, F., Li, S., Chen, L., Kounavis, M. E., and Chau, D. H · 2018
Cited alongside, same era.
Glow: Generative Flow With Invertible 1x1 Convolutions
Kingma, D. P. and Dhariwal, P · 2018
Cited alongside, same era.
Adversarial Examples for Generative Models
Kos, J., Fischer, I., and Song, D · 2018
Cited alongside, same era.
Towards Deep Learning Models Resistant to Adversarial Attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
Cited alongside, same era.
Adversarial Attacks on Neural Networks for Graph Data
Zügner, D., Akbarnejad, A., and Günnemann, S · 2018
Cited alongside, same era.
Understanding the Limitations of Conditional Generative Models
Fetaya, E., Jacobsen, J.-H., Grathwohl, W., and Zemel, R · 2019
Cited alongside, same era.
Class Action Complaint
Carr, S. and Jeffrey, N · 2022
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AI-Generated Art Sparks Furious Backlash from Japan’s Anime Community
Deck, A · 2022
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Copyright in Generative Deep Learning
Franceschelli, G. and Musolesi, M · 2022
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An Image Is Worth One Word: Personalizing Text-to-Image Generation Using Textual Inversion
Gal, R., Alaluf, Y., Atzmon, Y., Patashnik, O., Bermano, A. H., Chechik, G., and Cohen-Or, D · 2022
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Imagic: Text-Based Real Image Editing With Diffusion Models
Kawar, B., Zada, S., Lang, O., Tov, O., Chang, H., Dekel, T., Mosseri, I., and Irani, M · 2022
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DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps
Lu, C., Zhou, Y., Bao, F., Chen, J., Li, C., and Zhu, J · 2022
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How AI Art Can Free Artists, Not Replace Them
MT, D · 2022
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Diffusion Models for Adversarial Purification
Nie, W., Guo, B., Huang, Y., Xiao, C., Vahdat, A., and Anandkumar, A · 2022
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Dreamfusion: Text-to-3D Using 2D Diffusion
Poole, B., Jain, A., Barron, J. T., and Mildenhall, B · 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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Dreambooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation
Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., and Aberman, K · 2022
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Laion-5b: An Open Large-Scale Dataset for Training Next Generation Image-Text Models
Schuhmann, C., Beaumont, R., Vencu, R., Gordon, C., Wightman, R., Cherti, M., Coombes, T., Katta, A., Mullis, C., Wortsman, M., et al · 2022
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The Scary Truth About AI Copyright Is Nobody Knows What Will Happen Next
Vincent, J · 2022
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Diffusion probabilistic modeling for video generation
Yang, R., Srivastava, P., and Mandt, S · 2022
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