Variational Autoencoders and their many variants have displayed impressive
ability to perform dimensionality reduction, often achieving state-of-the-art
performance. Many current methods however, struggle to learn good
representations in High Dimensional, Low Sample Size (HDLSS) tasks, which is an
inherently challenging setting. We address this challenge by using an ensemble
of lightweight VAEs to learn posteriors over subsets of the feature-space,
which get aggregated into a joint posterior in a novel divide-and-conquer
approach. Specifically, we present an alternative factorisation of the joint
posterior that induces a form of implicit data augmentation that yields greater
sample efficiency. Through a series of experiments on eight real-world
datasets, we show that our method learns better latent representations in HDLSS
settings, which leads to higher accuracy in a downstream classification task.
Furthermore, we verify that our approach has a positive effect on
disentanglement and achieves a lower estimated Total Correlation on learnt
representations. Finally, we show that our approach is robust to partial
features at inference, exhibiting little performance degradation even with most
features missing.

本研究提出了一种使用轻量级 Variational Autoencoders（VAEs）的集成方法，以学习特征空间的子集上的后验概率，并将其聚合成一个联合后验概率，该方法呈现出隐性数据增广和更好地学习 HDSS 环境下的潜在表征，进而在下游分类任务中取得更高的准确性。

增强集成 VAE 的分治法在高维度小样本表格数据的表征学习中的应用

Enhancing Representation Learning on High-Dimensional, Small-Size  Tabular Data: A Divide and Conquer Method with Ensembled VAEs

Training of generative models especially Generative Adversarial Networks can
easily diverge in low-data setting. To mitigate this issue, we propose a novel
implicit data augmentation approach which facilitates stable training and
synthesize high-quality samples without need of label information.
Specifically, we view the discriminator as a metric embedding of the real data
manifold, which offers proper distances between real data points. We then
utilize information in the feature space to develop a fully unsupervised and
data-driven augmentation method. Experiments on few-shot generation tasks show
the proposed method significantly improve results from strong baselines with
hundreds of training samples.

本文提出了一种新的潜在数据增强方法，利用无监督的学习方式在低数据量的情况下，针对生成模型中容易发散的问题进行了优化，实现了有效而稳定的训练，并生成了高质量的样本。实验结果显示，该方法在少样例学习生成任务中取得了很好的效果。