Adversarial Learning

ArmGAN: Adversarial Representation Learning for Network Embedding

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ArmGAN: Adversarial Representation Learning for Network Embedding

Network embedding aims to learn low-dimensional representations of nodes in a network, which can be used for many downstream network analysis tasks. Recently, many network embedding methods based on Generative Adversarial Networks (GANs) have been proposed. However, GAN-based methods mainly face two challenges: (1) Existing GAN-based methods often use GANs to learn Gaussian distributions as … Read more

Comprehensive Explanation of Mathematical Principles of Generative Adversarial Networks (GANs)

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Comprehensive Explanation of Mathematical Principles of Generative Adversarial Networks (GANs)

Follow the public account “ML_NLP“ Set as “Starred“, heavy content delivered first! Reprinted from | PaperWeekly ©PaperWeekly Original · Author｜Sun Yudao School｜PhD student at Beijing University of Posts and Telecommunications Research Direction｜GAN Image Generation, Emotion Adversarial Sample Generation Paper Title: A Mathematical Introduction to Generative Adversarial Nets Paper Link: https://arxiv.org/abs/2009.00169 Introduction Since the pioneering work … Read more