论文标题
改善基于VAE的表示学习
Improving VAE-based Representation Learning
论文作者
论文摘要
诸如变分自动编码器（VAE）之类的潜在变量模型通常用于学习图像的表示形式。但是，对于诸如语义分类之类的下游任务，VAE所学的表示形式不如其他非lantent变量模型竞争。这导致了一些猜测，即潜在变量模型从根本上可能不适合表示学习。在这项工作中，我们研究了良好表示需要哪些属性，以及不同的VAE结构选择如何影响学习的特性。我们表明，通过使用更喜欢学习本地功能的解码器，潜在可以很好地捕获其余的全局功能，从而大大提高了下游分类任务的性能。我们进一步将提出的模型应用于半监督的学习任务，并证明数据效率的提高。
Latent variable models like the Variational Auto-Encoder (VAE) are commonly used to learn representations of images. However, for downstream tasks like semantic classification, the representations learned by VAE are less competitive than other non-latent variable models. This has led to some speculations that latent variable models may be fundamentally unsuitable for representation learning. In this work, we study what properties are required for good representations and how different VAE structure choices could affect the learned properties. We show that by using a decoder that prefers to learn local features, the remaining global features can be well captured by the latent, which significantly improves performance of a downstream classification task. We further apply the proposed model to semi-supervised learning tasks and demonstrate improvements in data efficiency.
