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Diffusion models have exhibited excellent performance in various domains.
A family of embedded Runge-Kutta formulae
Dormand, J. R. and Prince, P. J · 1980
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Reverse-time diffusion equation models
Anderson, B. D · 1982
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A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines
Hutchinson, M. F · 1990
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ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L., Li, K., and Fei-Fei, L · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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A connection between score matching and denoising autoencoders
Vincent, P · 2011
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RNADE: The real-valued neural autoregressive density-estimator
Uria, B., Murray, I., and Larochelle, H · 2013
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2014
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Importance weighted autoencoders
Burda, Y., Grosse, R., and Salakhutdinov, R · 2015
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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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Conditional image generation with pixelcnn decoders
Oord, A. v. d., Kalchbrenner, N., Vinyals, O., Espeholt, L., Graves, A., and Kavukcuoglu, K · 2016
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Density estimation using real nvp
Dinh, L., Sohl-Dickstein, J., and Bengio, S · 2017
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Pixelcnn++: Improving the pixelcnn with discretized logistic mixture likelihood and other modifications
Salimans, T., Karpathy, A., Chen, X., and Kingma, D. P · 2017
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Jax: composable transformations of python+ numpy programs
Bradbury, J., Frostig, R., Hawkins, P., Johnson, M. J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., VanderPlas, J., Wanderman-Milne, S., et al · 2018
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Autoencoder-based network anomaly detection
Chen, Z., Yeo, C. K., Lee, B. S., and Lau, C. T · 2018
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Glow: generative flow with invertible 1 × \times 1 convolutions
Kingma, D. P. and Dhariwal, P · 2018
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Ffjord: Free-form continuous dynamics for scalable reversible generative models
Grathwohl, W., Chen, R. T., Bettencourt, J., Sutskever, I., and Duvenaud, D · 2019
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Flow++: Improving flow-based generative models with variational dequantization and architecture design
Ho, J., Chen, X., Srinivas, A., Duan, Y., and Abbeel, P · 2019
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Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2019
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Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 2019
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Anomaly detection in trajectory data with normalizing flows
Dias, M. L., Mattos, C. L. C., da Silva, T. L., de Macedo, J. A. F., and Silva, W. C · 2020
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How to train your neural ode: the world of jacobian and kinetic regularization
Finlay, C., Jacobsen, J.-H., Nurbekyan, L., and Oberman, A · 2020
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Building normalizing flows with stochastic interpolants
Albergo, M. S. and Vanden-Eijnden, E · 2022
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Density ratio estimation via infinitesimal classification
Choi, K., Meng, C., Song, Y., and Ermon, S · 2022
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Diffusion posterior sampling for general noisy inverse problems
Chung, H., Kim, J., Mccann, M. T., Klasky, M. L., and Ye, J. C · 2022
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Imagen video: High definition video generation with diffusion models
Ho, J., Chan, W., Saharia, C., Whang, J., Gao, R., Gritsenko, A., Kingma, D. P., Poole, B., Norouzi, M., Fleet, D. J., et al · 2022
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Elucidating the design space of diffusion-based generative models
Karras, T., Aittala, M., Aila, T., and Laine, S · 2022
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Denoising diffusion restoration models
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Helminger, L., Djelouah, A., Gross, M., and Schroers, C · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
Cited alongside, same era.
Input complexity and out-of-distribution detection with likelihood-based generative models
Serrà, J., Álvarez, D., Gómez, V., Slizovskaia, O., Núñez, J. F., and Luque, J · 2020
Cited alongside, same era.
Sliced score matching: A scalable approach to density and score estimation
Song, Y., Garg, S., Shi, J., and Ermon, S · 2020
Cited alongside, same era.
Nvae: a deep hierarchical variational autoencoder
Vahdat, A. and Kautz, J · 2020
Cited alongside, same era.
Likelihood regret: an out-of-distribution detection score for variational auto-encoder
Xiao, Z., Yan, Q., and Amit, Y · 2020
Cited alongside, same era.
Wavegrad: Estimating gradients for waveform generation
Chen, N., Zhang, Y., Zen, H., Weiss, R. J., Norouzi, M., and Chan, W · 2021
Cited alongside, same era.
Kawar, B., Elad, M., Ermon, S., and Song, J · 2022
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Soft truncation: A universal training technique of score-based diffusion model for high precision score estimation
Kim, D., Shin, S., Song, K., Kang, W., and Moon, I.-C · 2022
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Flow matching for generative modeling
Lipman, Y., Chen, R. T., Ben-Hamu, H., Nickel, M., and Le, M · 2022
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SDEdit: Image synthesis and editing with stochastic differential equations
Meng, C., Song, Y., Song, J., Wu, J., Zhu, J.-Y., and Ermon, S · 2022
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Glide: Towards photorealistic image generation and editing with text-guided diffusion models
Nichol, A. Q., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., Mcgrew, B., Sutskever, I., and Chen, M · 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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Progressive distillation for fast sampling of diffusion models
Salimans, T. and Ho, J · 2022
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Poisson flow generative models
Xu, Y., Liu, Z., Tegmark, M., and Jaakkola, T. S · 2022
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Lossy image compression with conditional diffusion models
Yang, R. and Mandt, S · 2022
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Egsde: Unpaired image-to-image translation via energy-guided stochastic differential equations
Zhao, M., Bao, F., Li, C., and Zhu, J · 2022
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Pfgm++: Unlocking the potential of physics-inspired generative models
Xu, Y., Liu, Z., Tian, Y., Tong, S., Tegmark, M., and Jaakkola, T · 2023
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