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We prove fast mixing and characterize the stationary distribution of the Langevin Algorithm for inverting random weighted DNN generators.
Estimation of non-normalized statistical models by score matching
Hyvärinen, A · 2005
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T · 2015
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Compressed sensing using generative models
Bora, A., Jalal, A., Price, E., and Dimakis, A. G · 2017
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Non-convex learning via stochastic gradient langevin dynamics: a nonasymptotic analysis
Raginsky, M., Rakhlin, A., and Telgarsky, M · 2017
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Global convergence of langevin dynamics based algorithms for nonconvex optimization, 2017
Xu, P., Chen, J., Zou, D., and Gu, Q · 2017
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Modeling sparse deviations for compressed sensing using generative models
Dhar, M., Grover, A., and Ermon, S · 2018
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Phase retrieval under a generative prior
Hand, P., Leong, O., and Voroninski, V · 2018
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Deep decoder: Concise image representations from untrained non-convolutional networks
Heckel, R. and Hand, P · 2018
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A provably convergent scheme for compressive sensing under random generative priors, 12 2018
Huang, W., Hand, P., Heckel, R., and Voroninski, V · 2018
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Correction by projection: Denoising images with generative adversarial networks
Tripathi, S., Lipton, Z. C., and Nguyen, T. Q · 2018
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High-dimensional probability: An introduction with applications in data science , volume 47
Vershynin, R · 2018
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Invertible generative models for inverse problems: mitigating representation error and dataset bias
Asim, M., Ahmed, A., and Hand, P · 2019
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Seeing what a gan cannot generate, 2019
Bau, D., Zhu, J.-Y., Wulff, J., Peebles, W., Strobelt, H., Zhou, B., and Torralba, A · 2019
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Large scale gan training for high fidelity natural image synthesis, 2019
Brock, A., Donahue, J., and Simonyan, K · 2019
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Lower bounds for compressed sensing with generative models
Kamath, A., Karmalkar, S., and Price, E · 2019
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A style-based generator architecture for generative adversarial networks
Karras, T., Laine, S., and Aila, T · 2019
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Coherent semantic attention for image inpainting
Liu, H., Jiang, B., Xiao, Y., and Yang, C · 2019
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Image synthesis with a single (robust) classifier, 2019
Santurkar, S., Tsipras, D., Tran, B., Ilyas, A., Engstrom, L., and Madry, A · 2019
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Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 2019
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Theoretical guarantees for sampling and inference in generative models with latent diffusions
Tzen, B. and Raginsky, M · 2019
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Deep learning for single image super-resolution: A brief review
Yang, W., Zhang, X., Tian, Y., Wang, W., Xue, J.-H., and Liao, Q · 2019
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Free-form image inpainting with gated convolution
Yu, J., Lin, Z., Yang, J., Shen, X., Lu, X., and Huang, T · 2019
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Fast mixing of multi-scale langevin dynamics under the manifold hypothesis, 2020
Block, A., Mroueh, Y., Rakhlin, A., and Ross, J · 2020
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Your local gan: Designing two dimensional local attention mechanisms for generative models
Daras, G., Odena, A., Zhang, H., and Dimakis, A. G · 2020
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Constant-expansion suffices for compressed sensing with generative priors
Daskalakis, C., Rohatgi, D., and Zampetakis, E · 2020
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Constant-expansion suffices for compressed sensing with generative priors. in the
Daskalakis, C., Rohatgi, D., and Zampetakis, M · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Measuring robustness in deep learning based compressive sensing
Darestani, M. Z., Chaudhari, A., and Heckel, R · 2021
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Diffusion models beat gans on image synthesis, 2021
Dhariwal, P. and Nichol, A · 2021
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A provably convergent scheme for compressive sensing under random generative priors
Huang, W., Hand, P., Heckel, R., and Voroninski, V · 2021
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Gotta go fast when generating data with score-based models, 2021
Jolicoeur-Martineau, A., Li, K., Piché-Taillefer, R., Kachman, T., and Mitliagkas, I · 2021
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Snips: Solving noisy inverse problems stochastically
Kawar, B., Vaksman, G., and Elad, M · 2021
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Prior image-constrained reconstruction using style-based generative models
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Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Compressed sensing with approximate priors via conditional resampling
Jalal, A., Karmalkar, S., Dimakis, A., and Price, E · 2020
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Analyzing and improving the image quality of stylegan
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., and Aila, T · 2020
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Information-theoretic lower bounds for compressive sensing with generative models
Liu, Z. and Scarlett, J · 2020
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Pulse: Self-supervised photo upsampling via latent space exploration of generative models
Menon, S., Damian, A., Hu, S., Ravi, N., and Rudin, C · 2020
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Deep learning techniques for inverse problems in imaging
Ongie, G., Jalal, A., Metzler, C. A., Baraniuk, R. G., Dimakis, A. G., and Willett, R · 2020
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Kelkar, V. A. and Anastasio, M. A · 2021
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Sdedit: Guided image synthesis and editing with stochastic differential equations, 2021
Meng, C., He, Y., Song, Y., Song, J., Wu, J., Zhu, J.-Y., and Ermon, S · 2021
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Provable compressed sensing with generative priors via langevin dynamics, 2021
Nguyen, T. V., Jagatap, G., and Hegde, C · 2021
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Improved denoising diffusion probabilistic models, 2021
Nichol, A. and Dhariwal, P · 2021
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Glide: Towards photorealistic image generation and editing with text-guided diffusion models, 2021
Nichol, A., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., McGrew, B., Sutskever, I., and Chen, M · 2021
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Learning transferable visual models from natural language supervision, 2021
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., and Sutskever, I · 2021
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Score-based generative modeling in latent space
Vahdat, A., Kreis, K., and Kautz, J · 2021
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Learning to efficiently sample from diffusion probabilistic models, 2021
Watson, D., Ho, J., Norouzi, M., and Chan, W · 2021
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Deblurring via stochastic refinement, 2021
Whang, J., Delbracio, M., Talebi, H., Saharia, C., Dimakis, A. G., and Milanfar, P · 2021
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Tackling the generative learning trilemma with denoising diffusion gans, 2021
Xiao, Z., Kreis, K., and Vahdat, A · 2021
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Elucidating the design space of diffusion-based generative models, 2022
Karras, T., Aittala, M., Aila, T., and Laine, S · 2022
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Denoising diffusion restoration models, 2022
Kawar, B., Elad, M., Ermon, S., and Song, J · 2022
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3d gan inversion for controllable portrait image animation, 2022
Lin, C. Z., Lindell, D. B., Chan, E. R., and Wetzstein, G · 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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Photorealistic text-to-image diffusion models with deep language understanding, 2022
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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Progressive distillation for fast sampling of diffusion models
Salimans, T. and Ho, J · 2022
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