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Unsupervised Visual Representation Learning by Context ...

Unsupervised Visual Representation Learning by Context PredictionCarl Doersch1,2 Abhinav Gupta1 Alexei A. Efros21 School of Computer Science2 Dept. of Electrical Engineering and Computer ScienceCarnegie Mellon UniversityUniversity of California, BerkeleyAbstractThis work explores the use of spatial Context as a sourceof free and plentiful supervisory signal for training a richvisual Representation . Given only a large, unlabeled imagecollection, we extract random pairs of patches from eachimage and train a convolutional neural net to predict the po-sition of the second patch relative to the first. We argue thatdoing well on this task requires the model to learn to recog-nize objects and their parts.

the appearance of an image region by consensus voting of the transitive nearest neighbors of its surrounding regions. Our previous work [12] explicitly formulates a statistical test to determine whether the data is better explained by a prediction or by a low-level null hypothesis model. The key problem that these approaches must address is

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