Sensory data are often comprised of independent content and transformation
factors. For example, face images may have shapes as content and poses as
transformation. To infer separately these factors from given data, various
``disentangling'' models have been proposed. However, many of these are
supervised or semi-supervised, either requiring attribute labels that are often
unavailable or disallowing for generalization over new contents. In this study,
we introduce a novel deep generative model, called group-based variational
autoencoders. In this, we assume no explicit labels, but a weaker form of
structure that groups together data instances having the same content but
transformed differently; we thereby separately estimate a group-common factor
as content and an instance-specific factor as transformation. This approach
allows for learning to represent a general continuous space of contents, which
can accommodate unseen contents. Despite the simplicity, our model succeeded in
learning, from five datasets, content representations that are highly separate
from the transformation representation and generalizable to data with novel
contents. We further provide detailed analysis of the latent content code and
show insight into how our model obtains the notable transformation invariance
and content generalizability.

本研究提出了一种基于组的变分自编码器模型，能够从多个数据集中无需标签的情况下，对数据进行解耦，并将其分为内容因素与变化因素，从而实现变换不变性和内容泛化能力。