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This paper aims at providing insight on the transferability of deep CNNfeatures to unsupervised problems. We study the impact of different pretrainedCNN feature extractors on the problem of image set clustering for objectclassification as well as fine-grained classification. We propose a ratherstraightforward pipeline combining deep-feature extraction using a CNNpretrained on ImageNet and a classic clustering algorithm to classify sets ofimages. This approach is compared to state-of-the-art algorithms inimage-clustering and provides better results. These results strengthen thebelief that supervised training of deep CNN on large datasets, with a largevariability of classes, extracts better features than most carefully designedengineering approaches, even for unsupervised tasks. We also validate ourapproach on a robotic application, consisting in sorting and storing objectssmartly based on clustering.

CNN features are also great at unsupervised classification