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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.

high-resolution natural images. Unsupervisedrepresentation learning can also be formu-lated as learning an embedding (i.e. a feature vector for each image) where images that are semantically similar are close, while semantically different ones are far apart. One way to build such a representation is to create a supervised

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