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Diffusion models have achieved remarkable success in generating high-quality images.
Decoding fingerprinting using the markov chain monte carlo method
Furon, T., Guyader, A., and Cérou, F · 2012
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Toward practical joint decoding of binary tardos fingerprinting codes
Meerwald, P. and Furon, T · 2012
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
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A dwt, dct and svd based watermarking technique to protect the image piracy
Rahman, M. M · 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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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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The unreasonable effectiveness of deep features as a perceptual metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., and Wang, O · 2018
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Hidden: Hiding data with deep networks
Zhu, J., Kaplan, R., Johnson, J., and Fei-Fei, L · 2018
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Efficientnet: Rethinking model scaling for convolutional neural networks
Tan, M. and Le, Q · 2019
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Robust invisible video watermarking with attention
Zhang, K. A., Xu, L., Cuesta-Infante, A., and Veeramachaneni, K · 2019
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Denoising diffusion implicit models
Song, J., Meng, C., and Ermon, S · 2020
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Emerging properties in self-supervised vision transformers
Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., and Joulin, A · 2021
Cited alongside, same era.
Lora: Low-rank adaptation of large language models
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W · 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
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
Cited alongside, same era.
Florence: A new foundation model for computer vision
Yuan, L., Chen, D., Chen, Y.-L., Codella, N., Dai, X., Gao, J., Hu, H., Huang, X., Li, B., Li, C., et al · 2021
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Wortsman, M., Ilharco, G., Gadre, S. Y., Roelofs, R., Gontijo-Lopes, R., Morcos, A. S., Namkoong, H., Farhadi, A., Carmon, Y., Kornblith, S., et al · 2022
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Scaling autoregressive models for content-rich text-to-image generation
Yu, J., Xu, Y., Koh, J. Y., Luong, T., Baid, G., Wang, Z., Vasudevan, V., Ku, A., Yang, Y., Ayan, B. K., et al · 2022
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Improving image generation with better captions
Betker, J., Goh, G., Jing, L., TimBrooks, Wang, J., Li, L., LongOuyang, JuntangZhuang, JoyceLee, YufeiGuo, WesamManassra, PrafullaDhariwal, CaseyChu, YunxinJiao, and Ramesh, A · 2023
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Rosteals: Robust steganography using autoencoder latent space
Bui, T., Agarwal, S., Yu, N., and Collomosse, J · 2023
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Diffusionshield: A watermark for copyright protection against generative diffusion models
Cui, Y., Ren, J., Xu, H., He, P., Liu, H., Sun, L., and Tang, J · 2023
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Cited alongside, same era.
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
Cited alongside, same era.
Classifier-free diffusion guidance
Ho, J. and Salimans, T · 2022
Cited alongside, same era.
Elucidating the design space of diffusion-based generative models
Karras, T., Aittala, M., Aila, T., and Laine, S · 2022
Cited alongside, same era.
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
Cited alongside, same era.
Hierarchical text-conditional image generation with clip latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M · 2022
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
Cited alongside, same era.
Photorealistic text-to-image diffusion models with deep language understanding
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E. L., Ghasemipour, K., Gontijo Lopes, R., Karagol Ayan, B., Salimans, T., et al · 2022
Cited alongside, same era.
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The stable signature: Rooting watermarks in latent diffusion models
Fernandez, P., Couairon, G., Jégou, H., Douze, M., and Furon, T · 2023
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Dreamsim: Learning new dimensions of human visual similarity using synthetic data
Fu, S., Tamir, N., Sundaram, S., Chai, L., Zhang, R., Dekel, T., and Isola, P · 2023
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Lcm-lora: A universal stable-diffusion acceleration module
Luo, S., Tan, Y., Patil, S., Gu, D., von Platen, P., Passos, A., Huang, L., Li, J., and Zhao, H · 2023
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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 · 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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Flexible and secure watermarking for latent diffusion model
Xiong, C., Qin, C., Feng, G., and Zhang, X · 2023
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Adding conditional control to text-to-image diffusion models
Zhang, L., Rao, A., and Agrawala, M · 2023
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