论文标题
改进的半监督VAE，用于学习解开表示形式
An Improved Semi-Supervised VAE for Learning Disentangled Representations
论文作者
论文摘要
在表示学习中学习易于解释的和解开的表示是一项至关重要但具有挑战性的任务。在这项工作中，我们专注于半监督的分解学习，并扩展了Locatello等人的工作。 （2019）引入了另一种监督来源，我们将其表示为标签替代品。具体而言，在培训期间，我们替换了与数据点相关的与其基地表示点相关的推断表示。从理论上讲，我们的扩展灵感来自于我们在VAE的背景下，我们提出的半监督分开学习的一般框架，这些框架自然地激发了现有半监督VAE中常用的监督术语（但不是用于分离的学习）。关于合成和真实数据集的广泛实验既是定量和定性的，既可以通过非常有限的监督来显着，始终如一地改善分离的能力。
Learning interpretable and disentangled representations is a crucial yet challenging task in representation learning. In this work, we focus on semi-supervised disentanglement learning and extend work by Locatello et al. (2019) by introducing another source of supervision that we denote as label replacement. Specifically, during training, we replace the inferred representation associated with a data point with its ground-truth representation whenever it is available. Our extension is theoretically inspired by our proposed general framework of semi-supervised disentanglement learning in the context of VAEs which naturally motivates the supervised terms commonly used in existing semi-supervised VAEs (but not for disentanglement learning). Extensive experiments on synthetic and real datasets demonstrate both quantitatively and qualitatively the ability of our extension to significantly and consistently improve disentanglement with very limited supervision.
