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Recently, Generative Diffusion Models (GDMs) have showcased their remarkable capabilities in learning and generating images.
Sparse coding with an overcomplete basis set: A strategy employed by v1?
Olshausen, B. A. and Field, D. J · 1997
Earlier work this paper cites.
Digital watermarking: algorithms and applications
Podilchuk, C. I. and Delp, E. J · 2001
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Digital watermarking
Cox, I., Miller, M., Bloom, J., and Honsinger, C · 2002
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Combinational image watermarking in the spatial and frequency domains
Shih, F. Y. and Wu, S. Y · 2003
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Dwt-dct-svd based watermarking
Navas, K., Ajay, M. C., Lekshmi, M., Archana, T. S., and Sasikumar, M · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Online learning for matrix factorization and sparse coding
Mairal, J., Bach, F., Ponce, J., and Sapiro, G · 2010
Earlier work this paper cites.
Robust data hiding using inverse gradient attention
Zhang, H., Wang, H., Cao, Y., Shen, C., and Li, Y · 2011
Earlier work this paper cites.
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
Earlier work this paper cites.
A latent variable model approach to pmi-based word embeddings
Arora, S., Li, Y., Liang, Y., Ma, T., and Risteski, A · 2016
Earlier work this paper cites.
Ms-celeb-1m: A dataset and benchmark for large-scale face recognition
Guo, Y., Zhang, L., Hu, Y., He, X., and Gao, J · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2017
Earlier work this paper cites.
The unreasonable effectiveness of deep features as a perceptual metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., and Wang, O · 2018
Earlier work this paper cites.
Hidden: Hiding data with deep networks
Zhu, J., Kaplan, R., Johnson, J., and Fei-Fei, L · 2018
Cited alongside, same era.
Generalization of two-layer neural networks: An asymptotic viewpoint
Ba, J., Erdogdu, M., Suzuki, T., Wu, D., and Zhang, T · 2019
Cited alongside, same era.
High-capacity convolutional video steganography with temporal residual modeling
Weng, X., Li, Y., Chi, L., and Mu, Y · 2019
Cited alongside, same era.
Invisible steganography via generative adversarial networks
Zhang, R., Dong, S., and Liu, J · 2019
Cited alongside, same era.
A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
Cited alongside, same era.
Generative adversarial networks
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2020
Cited alongside, same era.
Artificial fingerprinting for generative models: Rooting deepfake attribution in training data
Yu, N., Skripniuk, V., Abdelnabi, S., and Fritz, M · 2021
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Feature purification: How adversarial training performs robust deep learning
Allen-Zhu, Z. and Li, Y · 2022
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Classifier-free diffusion guidance
Ho, J. and Salimans, T · 2022
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Towards blind watermarking: Combining invertible and non-invertible mechanisms
Ma, R., Guo, M., Hou, Y., Yang, F., Li, Y., Jia, H., and Xie, X · 2022
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Pokemon blip captions
Pinkney, J. N. M · 2022
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Hierarchical text-conditional image generation with clip latents
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
Cited alongside, same era.
A review on implementation of digital image watermarking techniques using lsb and dwt
Kumar, A · 2020
Cited alongside, same era.
Denoising diffusion implicit models
Song, J., Meng, C., and Ermon, S · 2020
Cited alongside, same era.
Stegastamp: Invisible hyperlinks in physical photographs
Tancik, M., Mildenhall, B., and Ng, R · 2020
Cited alongside, same era.
Watermarking neural networks with watermarked images
Wu, H., Liu, G., Yao, Y., and Zhang, X · 2020
Cited alongside, same era.
Mbrs: Enhancing robustness of dnn-based watermarking by mini-batch of real and simulated jpeg compression
Jia, Z., Fang, H., and Zhang, W · 2021
Cited alongside, same era.
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M · 2022
Later among the works it cites.
High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
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Generating high fidelity data from low-density regions using diffusion models
Sehwag, V., Hazirbas, C., Gordo, A., Ozgenel, F., and Canton, C · 2022
Later among the works it cites.
Diffusers: State-of-the-art diffusion models
von Platen, P., Patil, S., Lozhkov, A., Cuenca, P., Lambert, N., Rasul, K., Davaadorj, M., and Wolf, T · 2022
Later among the works it cites.
Consistent diffusion models: Mitigating sampling drift by learning to be consistent
Daras, G., Dagan, Y., Dimakis, A. G., and Daskalakis, C · 2023
Closest in time.
Don’t play favorites: Minority guidance for diffusion models
Um, S. and Ye, J. C · 2023
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Ai art copyright lawsuit: Getty images and stable diffusion
Vincent, J · 2023
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Adding conditional control to text-to-image diffusion models
Zhang, L. and Agrawala, M · 2023
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