  Pre-training general-purpose visual features with convolutional neural
networks without relying on annotations is a challenging and important task.
Most recent efforts in unsupervised feature learning have focused on either
small or highly curated datasets like ImageNet, whereas using uncurated raw
datasets was found to decrease the feature quality when evaluated on a transfer
task. Our goal is to bridge the performance gap between unsupervised methods
trained on curated data, which are costly to obtain, and massive raw datasets
that are easily available. To that effect, we propose a new unsupervised
approach which leverages self-supervision and clustering to capture
complementary statistics from large-scale data. We validate our approach on 96
million images from YFCC100M, achieving state-of-the-art results among
unsupervised methods on standard benchmarks, which confirms the potential of
unsupervised learning when only uncurated data are available. We also show that
pre-training a supervised VGG-16 with our method achieves 74.9% top-1
classification accuracy on the validation set of ImageNet, which is an
improvement of +0.8% over the same network trained from scratch. Our code is
available at https://github.com/facebookresearch/DeeperCluster.
