This paper aims at providing insight on the transferability of deep CNN
features to unsupervised problems. We study the impact of different pretrained
CNN feature extractors on the problem of image set clustering for object
classification as well as fine-grained classification. We propose a rather
straightforward pipeline combining deep-feature extraction using a CNN
pretrained on ImageNet and a classic clustering algorithm to classify sets of
images. This approach is compared to state-of-the-art algorithms in
image-clustering and provides better results. These results strengthen the
belief that supervised training of deep CNN on large datasets, with a large
variability of classes, extracts better features than most carefully designed
engineering approaches, even for unsupervised tasks. We also validate our
approach on a robotic application, consisting in sorting and storing objects
smartly based on clustering.

本文旨在探讨深度卷积神经网络特征对无监督问题的可迁移性，并研究不同的预训练卷积神经网络特征提取器对于图像集群聚类和细粒度分类问题的影响，提出结合预训练的 ImageNet 中的深度特征提取和经典的聚类算法管道的分类方法，在图像聚类方面优于现有算法。作者同时在机器人应用中验证了该方法，成功实现了一种智能物体分类和存储的方案。

CNN 特征在无监督分类方面表现出色

CNN features are also great at unsupervised classification

The recent rapid and tremendous success of deep convolutional neural networks
(CNN) on many challenging computer vision tasks largely derives from the
accessibility of the well-annotated ImageNet and PASCAL VOC datasets.
Nevertheless, unsupervised image categorization (i.e., without the ground-truth
labeling) is much less investigated, yet critically important and difficult
when annotations are extremely hard to obtain in the conventional way of
"Google Search" and crowd sourcing. We address this problem by presenting a
looped deep pseudo-task optimization (LDPO) framework for joint mining of deep
CNN features and image labels. Our method is conceptually simple and rests upon
the hypothesized "convergence" of better labels leading to better trained CNN
models which in turn feed more discriminative image representations to
facilitate more meaningful clusters/labels. Our proposed method is validated in
tackling two important applications: 1) Large-scale medical image annotation
has always been a prohibitively expensive and easily-biased task even for
well-trained radiologists. Significantly better image categorization results
are achieved via our proposed approach compared to the previous
state-of-the-art method. 2) Unsupervised scene recognition on representative
and publicly available datasets with our proposed technique is examined. The
LDPO achieves excellent quantitative scene classification results. On the MIT
indoor scene dataset, it attains a clustering accuracy of 75.3%, compared to
the state-of-the-art supervised classification accuracy of 81.0% (when both are
based on the VGG-VD model).

本研究提出一种基于循环伪任务优化的框架，用于联合挖掘深度 CNN 特征和图像标签，并对大规模医学图像注释和无监督场景识别等应用进行了验证。