DeepAI AI Chat
Log In Sign Up

Adversarial Transfer Learning

by   Diane J. Cook, et al.
Washington State University

There is a recent large and growing interest in generative adversarial networks (GANs), which offer powerful features for generative modeling, density estimation, and energy function learning. GANs are difficult to train and evaluate but are capable of creating amazingly realistic, though synthetic, image data. Ideas stemming from GANs such as adversarial losses are creating research opportunities for other challenges such as domain adaptation. In this paper, we look at the field of GANs with emphasis on these areas of emerging research. To provide background for adversarial techniques, we survey the field of GANs, looking at the original formulation, training variants, evaluation methods, and extensions. Then we survey recent work on transfer learning, focusing on comparing different adversarial domain adaptation methods. Finally, we take a look forward to identify open research directions for GANs and domain adaptation, including some promising applications such as sensor-based human behavior modeling.


page 2

page 5

page 14

page 15

page 26

page 27


Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions

Generative Adversarial Networks (GANs) is a novel class of deep generati...

Mind the (optimality) Gap: A Gap-Aware Learning Rate Scheduler for Adversarial Nets

Adversarial nets have proved to be powerful in various domains including...

A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications

Generative adversarial networks (GANs) are a hot research topic recently...

Generative Adversarial Networks for Malware Detection: a Survey

Since their proposal in the 2014 paper by Ian Goodfellow, there has been...

Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation

Optimal Transport (OT) distances such as Wasserstein have been used in s...

A survey on GANs for computer vision: Recent research, analysis and taxonomy

In the last few years, there have been several revolutions in the field ...

Parametric Adversarial Divergences are Good Task Losses for Generative Modeling

Generative modeling of high dimensional data like images is a notoriousl...

Code Repositories


Everything about Transfer Learning and Domain Adaptation--迁移学习

view repo


Everything about Transfer Learning and Domain Adaptation--迁移学习

view repo