Deep feature spaces have the capacity to encode complex transformations of
their input data. However, understanding the relative feature-space
relationship between two transformed encoded images is difficult. For instance,
what is the relative feature space relationship between two rotated images?
What is decoded when we interpolate in feature space? Ideally, we want to
disentangle confounding factors, such as pose, appearance, and illumination,
from object identity. Disentangling these is difficult because they interact in
very nonlinear ways. We propose a simple method to construct a deep feature
space, with explicitly disentangled representations of several known
transformations. A person or algorithm can then manipulate the disentangled
representation, for example, to re-render an image with explicit control over
parameterized degrees of freedom. The feature space is constructed using a
transforming encoder-decoder network with a custom feature transform layer,
acting on the hidden representations. We demonstrate the advantages of explicit
disentangling on a variety of datasets and transformations, and as an aid for
traditional tasks, such as classification.

本文介绍了用自定义特征转换层构建深度特征空间，并通过显式解缠模型在多个数据集和转换上显示出明显优势，并提供传统任务（如分类）的帮助。