A class of recent approaches for generating images, called Generative
Adversarial Networks (GAN), have been used to generate impressively realistic
images of objects, bedrooms, handwritten digits and a variety of other image
modalities. However, typical GAN-based approaches require large amounts of
training data to capture the diversity across the image modality. In this
paper, we propose DeLiGAN -- a novel GAN-based architecture for diverse and
limited training data scenarios. In our approach, we reparameterize the latent
generative space as a mixture model and learn the mixture model's parameters
along with those of GAN. This seemingly simple modification to the GAN
framework is surprisingly effective and results in models which enable
diversity in generated samples although trained with limited data. In our work,
we show that DeLiGAN can generate images of handwritten digits, objects and
hand-drawn sketches, all using limited amounts of data. To quantitatively
characterize intra-class diversity of generated samples, we also introduce a
modified version of "inception-score", a measure which has been found to
correlate well with human assessment of generated samples.

DeLiGAN 是一种用于有限训练数据场景的新型 GAN 架构，通过将潜在生成空间重新参数化为混合模型并学习混合模型的参数以及 GAN 的参数，实现了在有限数据的情况下生成具有多样性的样本。

DeLiGAN: 用于多样化和有限数据的生成对抗网络

DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data

In this work, we present the Text Conditioned Auxiliary Classifier Generative
Adversarial Network, (TAC-GAN) a text to image Generative Adversarial Network
(GAN) for synthesizing images from their text descriptions. Former approaches
have tried to condition the generative process on the textual data; but allying
it to the usage of class information, known to diversify the generated samples
and improve their structural coherence, has not been explored. We trained the
presented TAC-GAN model on the Oxford-102 dataset of flowers, and evaluated the
discriminability of the generated images with Inception-Score, as well as their
diversity using the Multi-Scale Structural Similarity Index (MS-SSIM). Our
approach outperforms the state-of-the-art models, i.e., its inception score is
3.45, corresponding to a relative increase of 7.8% compared to the recently
introduced StackGan. A comparison of the mean MS-SSIM scores of the training
and generated samples per class shows that our approach is able to generate
highly diverse images with an average MS-SSIM of 0.14 over all generated
classes.

本研究提出了 “TAC-GAN” 模型，一种文本到图像的生成对抗网络；相较于以前的方法，该模型使用类信息，能够提高生成样本的多样性并改善其结构一致性，对 Oxford-102 的花卉数据进行训练并通过 Inception-Score 和 MS-SSIM 测评其性能，结果表明其性能超越现有的同类模型，其平均 MS-SSIM 值达到 0.14。