Objects are composed of a set of geometrically organized parts. We introduce
an unsupervised capsule autoencoder (SCAE), which explicitly uses geometric
relationships between parts to reason about objects. Since these relationships
do not depend on the viewpoint, our model is robust to viewpoint changes. SCAE
consists of two stages. In the first stage, the model predicts presences and
poses of part templates directly from the image and tries to reconstruct the
image by appropriately arranging the templates. In the second stage, SCAE
predicts parameters of a few object capsules, which are then used to
reconstruct part poses. Inference in this model is amortized and performed by
off-the-shelf neural encoders, unlike in previous capsule networks. We find
that object capsule presences are highly informative of the object class, which
leads to state-of-the-art results for unsupervised classification on SVHN (55%)
and MNIST (98.7%). The code is available at
this https URL

本研究介绍了一种使用几何关系推断物体的无监督胶囊自编码器（SCAE）。通过该模型，可以恢复物体的各部分的姿态和存在性，并且其物体胶囊存在性与物体类别高度相关，从而在 SVHN（55％）和 MNIST（98.7％）上实现最先进的无监督分类结果。