Generative adversarial networks are generative models that are capable of
replicating the implicit probability distribution of the input data with high
accuracy. Traditionally, GANs consist of a Generator and a Discriminator which
interact with each other to produce highly realistic artificial data.
Traditional GANs fall prey to the mode collapse problem, which means that they
are unable to generate the different variations of data present in the input
dataset. Recently, multiple generators have been used to produce more realistic
output by mitigating the mode collapse problem. We use this multiple generator
framework. The novelty in this paper lies in making the generators compete
against each other while interacting with the discriminator simultaneously. We
show that this causes a dramatic reduction in the training time for GANs
without affecting its performance.

利用多个生成器竞争、同时与鉴别器的交互来解决传统 GAN 中的模式崩溃问题，大幅度减少训练时间，同时不影响其性能。

GAN 生成器之间的共享损失

Shared Loss between Generators of GANs

Outlier detection is an important topic in machine learning and has been used
in a wide range of applications. In this paper, we approach outlier detection
as a binary-classification issue by sampling potential outliers from a uniform
reference distribution. However, due to the sparsity of data in
high-dimensional space, a limited number of potential outliers may fail to
provide sufficient information to assist the classifier in describing a
boundary that can separate outliers from normal data effectively. To address
this, we propose a novel Single-Objective Generative Adversarial Active
Learning (SO-GAAL) method for outlier detection, which can directly generate
informative potential outliers based on the mini-max game between a generator
and a discriminator. Moreover, to prevent the generator from falling into the
mode collapsing problem, the stop node of training should be determined when
SO-GAAL is able to provide sufficient information. But without any prior
information, it is extremely difficult for SO-GAAL. Therefore, we expand the
network structure of SO-GAAL from a single generator to multiple generators
with different objectives (MO-GAAL), which can generate a reasonable reference
distribution for the whole dataset. We empirically compare the proposed
approach with several state-of-the-art outlier detection methods on both
synthetic and real-world datasets. The results show that MO-GAAL outperforms
its competitors in the majority of cases, especially for datasets with various
cluster types or high irrelevant variable ratio.

本文提出了一个基于单目标生成对抗主动学习（SO-GAAL）方法的新型离群点检测算法，通过多个生成器（MO-GAAL）解决数据稀疏性和模式崩溃等问题，在合成和真实数据集上的实验结果表明，MO-GAAL 在各种情况下表现良好。

生成敌对性主动学习用于无监督异常检测

Generative Adversarial Active Learning for Unsupervised Outlier  Detection

We propose a new approach to train the Generative Adversarial Nets (GANs)
with a mixture of generators to overcome the mode collapsing problem. The main
intuition is to employ multiple generators, instead of using a single one as in
the original GAN. The idea is simple, yet proven to be extremely effective at
covering diverse data modes, easily overcoming the mode collapse and delivering
state-of-the-art results. A minimax formulation is able to establish among a
classifier, a discriminator, and a set of generators in a similar spirit with
GAN. Generators create samples that are intended to come from the same
distribution as the training data, whilst the discriminator determines whether
samples are true data or generated by generators, and the classifier specifies
which generator a sample comes from. The distinguishing feature is that
internal samples are created from multiple generators, and then one of them
will be randomly selected as final output similar to the mechanism of a
probabilistic mixture model. We term our method Mixture GAN (MGAN). We develop
theoretical analysis to prove that, at the equilibrium, the Jensen-Shannon
divergence (JSD) between the mixture of generators' distributions and the
empirical data distribution is minimal, whilst the JSD among generators'
distributions is maximal, hence effectively avoiding the mode collapse. By
utilizing parameter sharing, our proposed model adds minimal computational cost
to the standard GAN, and thus can also efficiently scale to large-scale
datasets. We conduct extensive experiments on synthetic 2D data and natural
image databases (CIFAR-10, STL-10 and ImageNet) to demonstrate the superior
performance of our MGAN in achieving state-of-the-art Inception scores over
latest baselines, generating diverse and appealing recognizable objects at
different resolutions, and specializing in capturing different types of objects
by generators.

本文提出了一种名为 Mixture GAN （MGAN）的新方法，该方法采用多个生成器的混合训练，旨在克服 mode collapsing 问题，通过理论分析得出结论，证明了在平衡状态下，多个生成器的分布和数据分布之间的 Jensen-Shannon 差异最小，生成器之间的 Jensen-Shannon 差异最大，从而有效地避免了 mode collapsing 问题，能够在多项实验中取得明显的优势。