We propose a new active learning by query synthesis approach using Generative
Adversarial Networks (GAN). Different from regular active learning, the
resulting algorithm adaptively synthesizes training instances for querying to
increase learning speed. We generate queries according to the uncertainty
principle, but our idea can work with other active learning principles. We
report results from various numerical experiments to demonstrate the
effectiveness the proposed approach. In some settings, the proposed algorithm
outperforms traditional pool-based approaches. To the best our knowledge, this
is the first active learning work using GAN.

本研究提出了一种通过使用生成式对抗网络 (GAN) 实现的新型主动学习方法，通过根据不确定性原理生成查询来增加学习速度，我们的方法可以与其他主动学习原理一起使用；我们报告了各种数值实验的结果以证明所提出的方法的有效性。在某些情况下，该算法的效果优于传统的基于池的方法。据我们所知，这是第一个使用 GAN 实现的主动学习方法。

生成对抗主动学习

Generative Adversarial Active Learning

In this paper, we consider the problem of actively learning a linear
classifier through query synthesis where the learner can construct artificial
queries in order to estimate the true decision boundaries. This problem has
recently gained a lot of interest in automated science and adversarial reverse
engineering for which only heuristic algorithms are known. In such
applications, queries can be constructed de novo to elicit information (e.g.,
automated science) or to evade detection with minimal cost (e.g., adversarial
reverse engineering). We develop a general framework, called dimension coupling
(DC), that 1) reduces a d-dimensional learning problem to d-1 low dimensional
sub-problems, 2) solves each sub-problem efficiently, 3) appropriately
aggregates the results and outputs a linear classifier, and 4) provides a
theoretical guarantee for all possible schemes of aggregation. The proposed
method is proved resilient to noise. We show that the DC framework avoids the
curse of dimensionality: its computational complexity scales linearly with the
dimension. Moreover, we show that the query complexity of DC is near optimal
(within a constant factor of the optimum algorithm). To further support our
theoretical analysis, we compare the performance of DC with the existing work.
We observe that DC consistently outperforms the prior arts in terms of query
complexity while often running orders of magnitude faster.

该文提出了一种通过查询合成主动学习线性分类器的通用框架，该框架称为维耦合（DC），并证明了该方法对于噪声具有弹性，避免了维度诅咒，并且在查询复杂度方面接近最优算法，具有比现有方法更好的性能。