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Normalizing Flows (NFs) are likelihood-based models for continuous inputs.
Generating long sequences with sparse transformers
Child, R., Gray, S., Radford, A., and Sutskever, I · 1904
Earlier work this paper cites.
Stabilizing generative adversarial networks: A survey
Wiatrak, M., Albrecht, S. V., and Nystrom, A · 1910
Earlier work this paper cites.
A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines
Hutchinson, M. F · 1989
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L., Li, K., and Li, F · 2009
Earlier work this paper cites.
Density estimation by dual ascent of the log-likelihood
Tabak, E. G. and Vanden-Eijnden, E · 2010
Earlier work this paper cites.
Noroozi, M · 2012
Earlier work this paper cites.
Nice: Non-linear independent components estimation
Dinh, L., Krueger, D., and Bengio, Y · 2014
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. C., and Bengio, Y · 2014
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2014
Earlier work this paper cites.
MADE: masked autoencoder for distribution estimation
Germain, M., Gregor, K., Murray, I., and Larochelle, H · 2015
Earlier work this paper cites.
Variational inference with normalizing flows
Rezende, D. J. and Mohamed, S · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E. A., Maheswaranathan, N., and Ganguli, S · 2015
Earlier work this paper cites.
Improved variational inference with inverse autoregressive flow
Kingma, D. P., Salimans, T., Jozefowicz, R., Chen, X., Sutskever, I., and Welling, M · 2016
Earlier work this paper cites.
Conditional image generation with pixelcnn decoders
van den Oord, A., Kalchbrenner, N., Espeholt, L., Kavukcuoglu, K., Vinyals, O., and Graves, A · 2016
Earlier work this paper cites.
Pixel recurrent neural networks
van den Oord, A., Kalchbrenner, N., and Kavukcuoglu, K · 2016
Earlier work this paper cites.
Density estimation using real NVP
Dinh, L., Sohl-Dickstein, J., and Bengio, S · 2017
Earlier work this paper cites.
Masked autoregressive flow for density estimation
Papamakarios, G., Murray, I., and Pavlakou, T · 2017
Earlier work this paper cites.
Neural discrete representation learning
van den Oord, A., Vinyals, O., and Kavukcuoglu, K · 2017
Earlier work this paper cites.
Attention is all you need.(nips), 2017
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
Earlier work this paper cites.
Neural ordinary differential equations
Chen, T. Q., Rubanova, Y., Bettencourt, J., and Duvenaud, D · 2018
Earlier work this paper cites.
Neural autoregressive flows
Huang, C., Krueger, D., Lacoste, A., and Courville, A. C · 2018
Earlier work this paper cites.
Glow: Generative flow with invertible 1x1 convolutions
Kingma, D. P. and Dhariwal, P · 2018
Earlier work this paper cites.
Large scale GAN training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K · 2019
Earlier work this paper cites.
Block neural autoregressive flow
Cao, N. D., Aziz, W., and Titov, I · 2019
Earlier work this paper cites.
Augmented neural odes
Dupont, E., Doucet, A., and Teh, Y. W · 2019
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FFJORD: free-form continuous dynamics for scalable reversible generative models
Grathwohl, W., Chen, R. T. Q., 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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A style-based generator architecture for generative adversarial networks
Karras, T., Laine, S., and Aila, T · 2019
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Generating high fidelity images with subscale pixel networks and multidimensional upscaling
Menick, J. and Kalchbrenner, N · 2019
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Generating diverse high-fidelity images with VQ-VAE-2
Razavi, A., van den Oord, A., and Vinyals, O · 2019
Classifier-free diffusion guidance
Ho, J. and Salimans, T · 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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Elucidating the design space of diffusion-based generative models
Karras, T., Aittala, M., Aila, T., and Laine, S · 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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simple diffusion: End-to-end diffusion for high resolution images
Hoogeboom, E., Heek, J., and Salimans, T · 2023
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Scalable adaptive computation for iterative generation
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Language models are few-shot learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D · 2020
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Generative pretraining from pixels
Chen, M., Radford, A., Child, R., Wu, J., Jun, H., Luan, D., and Sutskever, I · 2020
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Stargan v2: Diverse image synthesis for multiple domains
Choi, Y., Uh, Y., Yoo, J., and Ha, J · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network
Sherstinsky, A · 2020
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Instance-conditioned GAN
Casanova, A., Careil, M., Verbeek, J., Drozdzal, M., and Romero-Soriano, A · 2021
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Jabri, A., Fleet, D. J., and Chen, T · 2023
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Scaling up gans for text-to-image synthesis
Kang, M., Zhu, J., Zhang, R., Park, J., Shechtman, E., Paris, S., and Park, T · 2023
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Flow matching for generative modeling
Lipman, Y., Chen, R. T. Q., Ben-Hamu, H., Nickel, M., and Le, M · 2023
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Flow matching for generative modeling
Lipman, Y., Chen, R. T. Q., Ben-Hamu, H., Nickel, M., and Le, M · 2023
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Multisample flow matching: Straightening flows with minibatch couplings
Pooladian, A., Ben-Hamu, H., Domingo-Enrich, C., Amos, B., Lipman, Y., and Chen, R. T. Q · 2023
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Improved techniques for training consistency models
Song, Y. and Dhariwal, P · 2023
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Consistency models
Song, Y., Dhariwal, P., Chen, M., and Sutskever, I · 2023
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Neural flow diffusion models: Learnable forward process for improved diffusion modelling
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Generative modeling with phase stochastic bridge
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Dart: Denoising autoregressive transformer for scalable text-to-image generation
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Autoregressive image generation without vector quantization
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Transformer neural autoregressive flows
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Sdxl: Improving latent diffusion models for high-resolution image synthesis
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Autoregressive model beats diffusion: Llama for scalable image generation
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Visual autoregressive modeling: Scalable image generation via next-scale prediction
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Jetformer: An autoregressive generative model of raw images and text
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Open-sora: Democratizing efficient video production for all, 2024
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