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Vision transformers require a huge amount of labeled data to outperformconvolutional neural networks. However, labeling a huge dataset is a veryexpensive process. Self-supervised learning techniques alleviate this problemby learning features similar to supervised learning in an unsupervised way. Inthis paper, we propose a self-supervised technique PatchRot that is crafted forvision transformers. PatchRot rotates images and image patches and trains thenetwork to predict the rotation angles. The network learns to extract bothglobal and local features from an image. Our extensive experiments on differentdatasets showcase PatchRot training learns rich features which outperformsupervised learning and compared baseline.

PatchRot: A Self-Supervised Technique for Training Vision Transformers