  Over the last years, deep convolutional neural networks (ConvNets) have
transformed the field of computer vision thanks to their unparalleled capacity
to learn high level semantic image features. However, in order to successfully
learn those features, they usually require massive amounts of manually labeled
data, which is both expensive and impractical to scale. Therefore, unsupervised
semantic feature learning, i.e., learning without requiring manual annotation
effort, is of crucial importance in order to successfully harvest the vast
amount of visual data that are available today. In our work we propose to learn
image features by training ConvNets to recognize the 2d rotation that is
applied to the image that it gets as input. We demonstrate both qualitatively
and quantitatively that this apparently simple task actually provides a very
powerful supervisory signal for semantic feature learning. We exhaustively
evaluate our method in various unsupervised feature learning benchmarks and we
exhibit in all of them state-of-the-art performance. Specifically, our results
on those benchmarks demonstrate dramatic improvements w.r.t. prior
state-of-the-art approaches in unsupervised representation learning and thus
significantly close the gap with supervised feature learning. For instance, in
PASCAL VOC 2007 detection task our unsupervised pre-trained AlexNet model
achieves the state-of-the-art (among unsupervised methods) mAP of 54.4% that is
only 2.4 points lower from the supervised case. We get similarly striking
results when we transfer our unsupervised learned features on various other
tasks, such as ImageNet classification, PASCAL classification, PASCAL
segmentation, and CIFAR-10 classification. The code and models of our paper
will be published on: https://github.com/gidariss/FeatureLearningRotNet .
