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Adversarial Divergences are Good Task Losses for Generative Modeling., , , , и . CoRR, (2017)Parametric Adversarial Divergences are Good Task Losses for Generative Modeling., , , , , и . ICLR (Workshop), OpenReview.net, (2018)A Closer Look at the Optimization Landscapes of Generative Adversarial Networks, , , , и . (2019)cite arxiv:1906.04848.Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity., , , , и . NeurIPS, стр. 19095-19108. (2021)Adversarial Example Games., , , , , , и . NeurIPS, (2020)A Closer Look at the Optimization Landscapes of Generative Adversarial Networks., , , , и . ICLR, OpenReview.net, (2020)Online Adversarial Attacks., , , , , , и . ICLR, OpenReview.net, (2022)A Variational Inequality Perspective on Generative Adversarial Nets., , , и . CoRR, (2018)A Variational Inequality Perspective on Generative Adversarial Networks., , , , и . ICLR (Poster), OpenReview.net, (2019)Stochastic Extragradient: General Analysis and Improved Rates., , , и . AISTATS, том 151 из Proceedings of Machine Learning Research, стр. 7865-7901. PMLR, (2022)