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

We propose to tackle the mode collapse problem in generative adversarial
networks (GANs) by using multiple discriminators and assigning a different
portion of each minibatch, called microbatch, to each discriminator. We
gradually change each discriminator's task from distinguishing between real and
fake samples to discriminating samples coming from inside or outside its
assigned microbatch by using a diversity parameter $\alpha$. The generator is
then forced to promote variety in each minibatch to make the microbatch
discrimination harder to achieve by each discriminator. Thus, all models in our
framework benefit from having variety in the generated set to reduce their
respective losses. We show evidence that our solution promotes sample diversity
since early training stages on multiple datasets.

本文提出一种新的方法来解决生成对抗网络中的模式崩溃问题，即通过使用多个鉴别器和将每个微批次中的样本分配到不同的鉴别器来逐步改变鉴别器的任务，从而强制生成器创建多样的样本以减少损失。

microbatchGAN：多元敌对鉴别刺激多样性

microbatchGAN: Stimulating Diversity with Multi-Adversarial  Discrimination

Generating multiple categories of texts is a challenging task and draws more
and more attention. Since generative adversarial nets (GANs) have shown
competitive results on general text generation, they are extended for category
text generation in some previous works. However, the complicated model
structures and learning strategies limit their performance and exacerbate the
training instability. This paper proposes a category-aware GAN (CatGAN) which
consists of an efficient category-aware model for category text generation and
a hierarchical evolutionary learning algorithm for training our model. The
category-aware model directly measures the gap between real samples and
generated samples on each category, then reducing this gap will guide the model
to generate high-quality category samples. The Gumbel-Softmax relaxation
further frees our model from complicated learning strategies for updating
CatGAN on discrete data. Moreover, only focusing on the sample quality normally
leads the mode collapse problem, thus a hierarchical evolutionary learning
algorithm is introduced to stabilize the training procedure and obtain the
trade-off between quality and diversity while training CatGAN. Experimental
results demonstrate that CatGAN outperforms most of the existing
state-of-the-art methods.

本文提出了一种基于类别感知的 GAN 模型，通过分类感知模型和分层进化算法，实现高质量分类文本的生成，同时解决了传统 GAN 模型难以训练，样本崩塌等问题，并在实验中表现优于现有大多数同类模型。

CatGAN：基于分类感知的逐层进化学习的生成对抗网络用于类别文本生成

CatGAN: Category-aware Generative Adversarial Networks with Hierarchical  Evolutionary Learning for Category Text Generation

Generative Adversarial Networks have surprising ability for generating sharp
and realistic images, though they are known to suffer from the so-called mode
collapse problem. In this paper, we propose a new GAN variant called Mixture
Density GAN that while being capable of generating high-quality images,
overcomes this problem by encouraging the Discriminator to form clusters in its
embedding space, which in turn leads the Generator to exploit these and
discover different modes in the data. This is achieved by positioning Gaussian
density functions in the corners of a simplex, using the resulting Gaussian
mixture as a likelihood function over discriminator embeddings, and formulating
an objective function for GAN training that is based on these likelihoods. We
demonstrate empirically (1) the quality of the generated images in Mixture
Density GAN and their strong similarity to real images, as measured by the
Fr\'echet Inception Distance (FID), which compares very favourably with
state-of-the-art methods, and (2) the ability to avoid mode collapse and
discover all data modes.

本文提出了一种新型 GAN 变体 Mixutre Density GAN，通过在判别器嵌入空间中形成聚类来打破生成器的模式崩塌问题，从而发现不同的数据模式，并在生成高质量图像方面表现出色。