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We propose a gradient flow procedure for generative modeling by transporting particles from an initial source distribution to a target distribution, where the gradient field on the particles is given by a noise-adaptive Wasserstein Gradient of the Maximum Mean Discrepancy (MMD).
Integral probability metrics and their generating classes of functions
Muller, A · 1997
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The variational formulation of the fokker–planck equation
Jordan, R., Kinderlehrer, D., and Otto, F · 1998
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Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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A global geometric framework for nonlinear dimensionality reduction
Tenenbaum, J. B., Silva, V. d., and Langford, J. C · 2000
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Estimation of non-normalized statistical models by score matching
Hyvärinen, A · 2005
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Gradient Flows in Metric Spaces and in the Space of Probability Measures
Ambrosio, L., Gigli, N., and Savaré, G · 2008
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Optimal Transport: Old and New
Villani, C · 2008
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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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A kernel two-sample test
Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., and Smola, A · 2012
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Generative adversarial networks, 2014
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Going deeper with convolutions, 2014
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A · 2014
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Deep learning face attributes in the wild, 2015
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
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U-net: Convolutional networks for biomedical image segmentation, 2015
Ronneberger, O., Fischer, P., and Brox, T · 2015
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Optimal transport for applied mathematicians
Santambrogio, F · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics, 2015
Sohl-Dickstein, J., Weiss, E. A., Maheswaranathan, N., and Ganguli, S · 2015
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Testing the manifold hypothesis
Fefferman, C., Mitter, S., and Narayanan, H · 2016
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f-gan: Training generative neural samplers using variational divergence minimization, 2016
Nowozin, S., Cseke, B., and Tomioka, R · 2016
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Improved techniques for training gans, 2016
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
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Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop, 2016
Yu, F., Seff, A., Zhang, Y., Song, S., Funkhouser, T., and Xiao, J · 2016
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Wasserstein gan, 2017
Arjovsky, M., Chintala, S., and Bottou, L · 2017
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Generalization and equilibrium in generative adversarial nets (gans), 2017
Arora, S., Ge, R., Liang, Y., Ma, T., and Zhang, Y · 2017
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Improved training of wasserstein gans, 2017
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A · 2017
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Adam: A method for stochastic optimization, 2017
Kingma, D. P. and Ba, J · 2017
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On convergence and stability of gans, 2017
Kodali, N., Abernethy, J., Hays, J., and Kira, Z · 2017
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Mmd gan: Towards deeper understanding of moment matching network, 2017
Li, C.-L., Chang, W.-C., Cheng, Y., Yang, Y., and Póczos, B · 2017
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On gradient regularizers for mmd gans
Arbel, M., Sutherland, D. J., Bińkowski, M., and Gretton, A · 2018
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Learning generative models with sinkhorn divergences
Genevay, A., Peyre, G., and Cuturi, M · 2018
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Gans trained by a two time-scale update rule converge to a local nash equilibrium, 2018
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2018
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Variational wasserstein gradient flow, 2022
Fan, J., Zhang, Q., Taghvaei, A., and Chen, Y · 2022
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A neural tangent kernel perspective of gans, 2022
Franceschi, J.-Y., de Bézenac, E., Ayed, I., Chen, M., Lamprier, S., and Gallinari, P · 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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Score-based generative models detect manifolds
Pidstrigach, J · 2022
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Dreamfusion: Text-to-3d using 2d diffusion
Poole, B., Jain, A., Barron, J. T., and Mildenhall, B · 2022
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Which training methods for gans do actually converge?
Mescheder, L., Geiger, A., and Nowozin, S · 2018
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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Schölkopf, B. and Smola, A. J · 2018
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On the discrimination-generalization tradeoff in gans
Zhang, P., Liu, Q., Zhou, D., Xu, T., and He, X · 2018
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Maximum mean discrepancy gradient flow, 2019
Arbel, M., Korba, A., Salim, A., and Gretton, 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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Denoising diffusion probabilistic models, 2020
Ho, J., Jain, A., and Abbeel, P · 2020
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High-resolution image synthesis with latent diffusion models, 2022
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 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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Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models
Watson, J. L., Juergens, D., Bennett, N. R., Trippe, B. L., Yim, J., Eisenach, H. E., Ahern, W., Borst, A. J., Ragotte, R. J., Milles, L. F., et al · 2022
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Tackling the generative learning trilemma with denoising diffusion gans, 2022
Xiao, Z., Kreis, K., and Vahdat, A · 2022
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Fast inference in denoising diffusion models via mmd finetuning, 2023
Aiello, E., Valsesia, D., and Magli, E · 2023
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Neural wasserstein gradient flows for maximum mean discrepancies with riesz kernels, 2023
Altekrüger, F., Hertrich, J., and Steidl, G · 2023
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Convergence of denoising diffusion models under the manifold hypothesis, 2023
Bortoli, V. D · 2023
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Unifying gans and score-based diffusion as generative particle models, 2023
Franceschi, J.-Y., Gartrell, M., Santos, L. D., Issenhuth, T., de Bézenac, E., Chen, M., and Rakotomamonjy, A · 2023
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Posterior sampling based on gradient flows of the mmd with negative distance kernel, 2023
Hagemann, P., Hertrich, J., Altekrüger, F., Beinert, R., Chemseddine, J., and Steidl, G · 2023
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Deep generative wasserstein gradient flows, 2023
Heng, A., Ansari, A. F., and Soh, H · 2023
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Generative sliced mmd flows with riesz kernels, 2023
Hertrich, J., Wald, C., Altekrüger, F., and Hagemann, P · 2023
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Voicebox: Text-guided multilingual universal speech generation at scale
Le, M., Vyas, A., Shi, B., Karrer, B., Sari, L., Moritz, R., Williamson, M., Manohar, V., Adi, Y., Mahadeokar, J., et al · 2023
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Adversarial diffusion distillation
Sauer, A., Lorenz, D., Blattmann, A., and Rombach, R · 2023
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Song, Y., Dhariwal, P., Chen, M., and Sutskever, I · 2023
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Ufogen: You forward once large scale text-to-image generation via diffusion gans
Xu, Y., Zhao, Y., Xiao, Z., and Hou, T · 2023
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Eliminating lipschitz singularities in diffusion models, 2023
Yang, Z., Feng, R., Zhang, H., Shen, Y., Zhu, K., Huang, L., Zhang, Y., Liu, Y., Zhao, D., Zhou, J., and Cheng, F · 2023
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On error propagation of diffusion models, 2024
Li, Y. and van der Schaar, M · 2024
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