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We propose a novel theoretical framework of analysis for Generative Adversarial Networks (GANs).
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Multilayer feedforward networks with a nonpolynomial activation function can approximate any function
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The multivariate Faà di Bruno formula and multivariate Taylor expansions with explicit integral remainder term
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Sriperumbudur, B. K., Fukumizu, K., and Lanckriet, G. R. G · 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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Continuity equations and ODE flows with non-smooth velocity
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Generative adversarial nets
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Auto-encoding variational Bayes
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Stochastic backpropagation and approximate inference in deep generative models
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
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Variations on Barbălat’s lemma
Farkas, B. and Wegner, S.-A · 2016
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Goodfellow, I · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Stein variational gradient descent: A general purpose Bayesian inference algorithm
Liu, Q. and Wang, D · 2016
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f f -GAN: Training generative neural samplers using variational divergence minimization
Nowozin, S., Cseke, B., and Tomioka, R · 2016
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S · 2016
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Towards principled methods for training generative adversarial networks
Arjovsky, M. and Bottou, L · 2017
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Wasserstein generative adversarial networks
Arjovsky, M., Chintala, S., and Bottou, L · 2017
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Generalization and equilibrium in generative adversarial nets (GANs)
Arora, S., Ge, R., Liang, Y., Ma, T., and Zhang, Y · 2017
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New approximations to the principal real-valued branch of the Lambert W W -function
Iacono, R. and Boyd, J. P · 2017
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MMD GAN: Towards deeper understanding of moment matching network
Li, C.-L., Chang, W.-C., Cheng, Y., Yang, Y., and Páczos, B · 2017
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Lim, J. H. and Ye, J. C · 2017
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Approximation and convergence properties of generative adversarial learning
Liu, S., Bousquet, O., and Chaudhuri, K · 2017
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Least squares generative adversarial networks
Mao, X., Li, Q., Xie, H., Lau, R. Y. K., Wang, Z., and Paul Smolley, S · 2017
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The numerics of GANs
Mescheder, L., Nowozin, S., and Geiger, A · 2017
Frequency bias in neural networks for input of non-uniform density
Basri, R., Galun, M., Geifman, A., Jacobs, D., Kasten, Y., and Kritchman, S · 2020
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The equivalence between Stein variational gradient descent and black-box variational inference
Chu, C., Minami, K., and Fukumizu, K · 2020
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A mean-field analysis of two-player zero-sum games
Domingo-Enrich, C., Jelassi, S., Mensch, A., Rotskoff, G., and Bruna, J · 2020
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Spectra of the conjugate kernel and neural tangent kernel for linear-width neural networks
Fan, Z. and Wang, Z · 2020
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Disentangling feature and lazy training in deep neural networks
Geiger, M., Spigler, S., Jacot, A., and Wyart, M · 2020
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Unrolled generative adversarial networks
Metz, L., Poole, B., Pfau, D., and Sohl-Dickstein, J · 2017
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Kernel mean embedding of distributions: A review and beyond
Muandet, K., Fukumizu, K., Sriperumbudur, B., and Schölkopf, B · 2017
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VEEGAN: Reducing mode collapse in GANs using implicit variational learning
Srivastava, A., Valkov, L., Russell, C., Gutmann, M. U., and Sutton, C · 2017
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Amortised MAP inference for image super-resolution
Sønderby, C. K., Caballero, J., Theis, L., Shi, W., and Huszár, F · 2017
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JAX: composable transformations of Python+NumPy programs, 2018
Bradbury, J., Frostig, R., Hawkins, P., Johnson, M. J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., VanderPlas, J., Wanderman-Milne, S., and Zhang, Q · 2018
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Neural tangent kernel: Convergence and generalization in neural networks
Jacot, A., Gabriel, F., and Hongler, C · 2018
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Hron, J., Bahri, Y., Sohl-Dickstein, J., and Novak, R · 2020
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Why do deep residual networks generalize better than deep feedforward networks? — a neural tangent kernel perspective
Huang, K., Wang, Y., Tao, M., and Zhao, T · 2020
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Jain, N., Olmo, A., Sengupta, S., Manikonda, L., and Kambhampati, S · 2020
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Neural tangent kernels, transportation mappings, and universal approximation
Ji, Z., Telgarsky, M., and Xian, R · 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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Finite versus infinite neural networks: an empirical study
Lee, J., Schoenholz, S. S., Pennington, J., Adlam, B., Xiao, L., Novak, R., and Sohl-Dickstein, J · 2020
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On the linearity of large non-linear models: when and why the tangent kernel is constant
Liu, C., Zhu, L., and Belkin, M · 2020
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Neural Tangents: Fast and easy infinite neural networks in Python
Novak, R., Xiao, L., Hron, J., Lee, J., Alemi, A. A., Sohl-Dickstein, J., and Schoenholz, S. S · 2020
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On the infinite width limit of neural networks with a standard parameterization
Sohl-Dickstein, J., Novak, R., Schoenholz, S. S., and Lee, J · 2020
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Towards a better global loss landscape of GANs
Sun, R., Fang, T., and Schwing, A · 2020
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Fourier features let networks learn high frequency functions in low dimensional domains
Tancik, M., Srinivasan, P. P., Mildenhall, B., Fridovich-Keil, S., Raghavan, N., Singhal, U., Ramamoorthi, R., Barron, J. T., and Ng, R · 2020
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DeepFakes and beyond: A survey of face manipulation and fake detection
Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Morales, A., and Ortega-Garcia, J · 2020
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Tensor programs II: Neural tangent kernel for any architecture
Yang, G · 2020
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A type of generalization error induced by initialization in deep neural networks
Zhang, Y., Xu, Z.-Q. J., Luo, T., and Ma, Z · 2020
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The recurrent neural tangent kernel
Alemohammad, S., Wang, Z., Balestriero, R., and Baraniuk, R. G · 2021
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Understanding over-parameterization in generative adversarial networks
Balaji, Y., Sajedi, M., Kalibhat, N. M., Ding, M., Stöger, D., Soltanolkotabi, M., and Feizi, S · 2021
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Some theoretical insights into wasserstein GANs
Biau, G., Sangnier, M., and Tanielian, U · 2021
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Deep equals shallow for ReLU networks in kernel regimes
Bietti, A. and Bach, F · 2021
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Deep neural tangent kernel and Laplace kernel have the same RKHS
Chen, L. and Xu, S · 2021
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Neural tangent kernel maximum mean discrepancy
Cheng, X. and Xie, Y · 2021
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Generative adversarial networks for image and video synthesis: Algorithms and applications
Liu, M.-Y., Huang, X., Yu, J., Wang, T.-C., and Mallya, A · 2021
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On the convergence of gradient descent in GANs: MMD GAN as a gradient flow
Mroueh, Y. and Nguyen, T · 2021
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Generative adversarial networks in computer vision: A survey and taxonomy
Wang, Z., She, Q., and Ward, T. E · 2021
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Tensor programs iv: Feature learning in infinite-width neural networks
Yang, G. and Hu, E. J · 2021
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Hidden convexity of wasserstein GANs: Interpretable generative models with closed-form solutions
Sahiner, A., Ergen, T., Ozturkler, B., Bartan, B., Pauly, J. M., Mardani, M., and Pilanci, M · 2022
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Generalization error of GAN from the discriminator’s perspective
Yang, H. and E, W · 2022
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