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Learning to denoise has emerged as a prominent paradigm to design state-of-the-art deep generative models for natural images.
Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
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Clustering with bregman divergences
Banerjee, A., Merugu, S., Dhillon, I. S., Ghosh, J., and Lafferty, J · 2005
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Estimation of non-normalized statistical models by score matching
Hyvärinen, A · 2005
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Mixed poisson distributions
Karlis, D. and Xekalaki, E · 2005
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Offline handwriting recognition with multidimensional recurrent neural networks
Graves, A. and Schmidhuber, J · 2008
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A connection between score matching and denoising autoencoders
Vincent, P · 2011
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Beta-negative binomial process and Poisson factor analysis
Zhou, M., Hannah, L., Dunson, D., and Carin, L · 2012
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Auto-encoding variational Bayes
Kingma, D. P. and Welling, M · 2013
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S · 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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Ba, J. L., Kiros, J. R., and Hinton, G. E · 2016
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Hierarchical variational models
Ranganath, R., Tran, D., and Blei, D · 2016
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Improved techniques for training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
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Pixel recurrent neural networks
van Den Oord, A., Kalchbrenner, N., and Kavukcuoglu, K · 2016
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Wasserstein generative adversarial networks
Arjovsky, M., Chintala, S., and Bottou, L · 2017
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Improved training of Wasserstein GANs
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. C · 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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Variational inference using implicit distributions
Huszár, F · 2017
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Neural discrete representation learning
van den Oord, A., Vinyals, O., and Kavukcuoglu, K · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
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Which training methods for GANs do actually converge?
Mescheder, L., Geiger, A., and Nowozin, S · 2018
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Spectral normalization for generative adversarial networks
Miyato, T., Kataoka, T., Koyama, M., and Yoshida, Y · 2018
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Improving language understanding by generative pre-training
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al · 2018
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Semi-implicit variational inference
Yin, M. and Zhou, M · 2018
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Advances in variational inference
Zhang, C., Bütepage, J., Kjellström, H., and Mandt, S · 2018
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Large scale GAN training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K · 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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Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2019
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BIVA: A very deep hierarchy of latent variables for generative modeling
Maaløe, L., Fraccaro, M., Liévin, V., and Winther, O · 2019
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Doubly semi-implicit variational inference
Molchanov, D., Kharitonov, V., Sobolev, A., and Vetrov, D · 2019
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Data-efficient instance generation from instance discrimination
Yang, C., Shen, Y., Xu, Y., and Zhou, B · 2021
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Cold diffusion: Inverting arbitrary image transforms without noise
Bansal, A., Borgnia, E., Chu, H.-M., Li, J. S., Kazemi, H., Huang, F., Goldblum, M., Geiping, J., and Goldstein, T · 2022
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A continuous time framework for discrete denoising models
Campbell, A., Benton, J., Bortoli, V. D., Rainforth, T., Deligiannidis, G., and Doucet, A · 2022
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MaskGIT: Masked generative image transformer
Chang, H., Zhang, H., Jiang, L., Liu, C., and Freeman, W. T · 2022
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Soft diffusion: Score matching for general corruptions
Daras, G., Delbracio, M., Talebi, H., Dimakis, A. G., and Milanfar, P · 2022
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Computational optimal transport
Peyré, G. and Cuturi, M · 2019
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Generating diverse high-fidelity images with VQ-VAE-2
Razavi, A., Van den Oord, A., and Vinyals, O · 2019
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Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 2019
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Unbiased implicit variational inference
Titsias, M. K. and Ruiz, F · 2019
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Training generative adversarial networks with limited data
Karras, T., Aittala, M., Hellsten, J., Laine, S., Lehtinen, J., and Aila, T · 2020
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Vector quantized diffusion model for text-to-image synthesis
Gu, S., Chen, D., Bao, J., Wen, F., Zhang, B., Chen, D., Yuan, L., and Guo, B · 2022
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CARD: Classification and regression diffusion models
Han, X., Zheng, H., and Zhou, M · 2022
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Cascaded diffusion models for high fidelity image generation
Ho, J., Saharia, C., Chan, W., Fleet, D. J., Norouzi, M., and Salimans, T · 2022
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Global context with discrete diffusion in vector quantised modelling for image generation
Hu, M., Wang, Y., Cham, T.-J., Yang, J., and Suganthan, P. N · 2022
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Torsional diffusion for molecular conformer generation
Jing, B., Corso, G., Chang, J., Barzilay, R., and Jaakkola, T. S · 2022
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Diffusion-LM improves controllable text generation
Li, X. L., Thickstun, J., Gulrajani, I., Liang, P., and Hashimoto, T · 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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Antigen-specific antibody design and optimization with diffusion-based generative models for protein structures
Luo, S., Su, Y., Peng, X., Wang, S., Peng, J., and Ma, J · 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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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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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
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Progressive distillation for fast sampling of diffusion models
Salimans, T. and Ho, J · 2022
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StyleGAN-XL: Scaling StyleGAN to large diverse datasets
Sauer, A., Schwarz, K., and Geiger, A · 2022
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Diffusion-GAN: Training GANs with diffusion
Wang, Z., Zheng, H., He, P., Chen, W., and Zhou, M · 2022
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Tackling the generative learning trilemma with denoising diffusion GANs
Xiao, Z., Kreis, K., and Vahdat, A · 2022
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Muse: Text-to-image generation via masked generative transformers
Chang, H., Zhang, H., Barber, J., Maschinot, A., Lezama, J., Jiang, L., Yang, M.-H., Murphy, K., Freeman, W. T., Rubinstein, M., et al · 2023
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Analog bits: Generating discrete data using diffusion models with self-conditioning
Chen, T., ZHANG, R., and Hinton, G · 2023
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Blurring diffusion models
Hoogeboom, E. and Salimans, T · 2023
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Generative modelling with inverse heat dissipation
Rissanen, S., Heinonen, M., and Solin, A · 2023
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Diffusion policies as an expressive policy class for offline reinforcement learning
Wang, Z., Hunt, J. J., and Zhou, M · 2023
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Diffsound: Discrete diffusion model for text-to-sound generation
Yang, D., Yu, J., Wang, H., Wang, W., Weng, C., Zou, Y., and Yu, D · 2023
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Truncated diffusion probabilistic models and diffusion-based adversarial auto-encoders
Zheng, H., He, P., Chen, W., and Zhou, M · 2023
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