This paper is focused on studying the view-manifold structure in the feature
spaces implied by the different layers of Convolutional Neural Networks (CNN).
There are several questions that this paper aims to answer: Does the learned
CNN representation achieve viewpoint invariance? How does it achieve viewpoint
invariance? Is it achieved by collapsing the view manifolds, or separating them
while preserving them? At which layer is view invariance achieved? How can the
structure of the view manifold at each layer of a deep convolutional neural
network be quantified experimentally? How does fine-tuning of a pre-trained CNN
on a multi-view dataset affect the representation at each layer of the network?
In order to answer these questions we propose a methodology to quantify the
deformation and degeneracy of view manifolds in CNN layers. We apply this
methodology and report interesting results in this paper that answer the
aforementioned questions.

本论文针对卷积神经网络 (CNN) 不同层次特征空间中的视角流形结构展开了研究，提出了一种测量视角流形变形和退化的方法，在多视角数据集上，通过精调预训练 CNN，实验证明了学习到的 CNN 表征可以实现视角不变性，并探究了其实现机制。