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| == Introduction == | | == Introduction == |
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| This paper evaluated the performance of state-of-the-art self-supervision techniques on learning different parts of convolutional neural networks (CNNs).
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| The main idea of self-supervised learning to learn from unlabeled data by training CNNs without manual data, e.g., a picture of a dog without the label “dog”. In self-supervised learning, data generate ground truth labels per se by pretext tasks such as rotation estimation.
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| In this paper, different experiments have been designed to learn deep features without humans providing labelled data by employing only one image as well as the whole dataset.
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| == Previous Work == | | == Previous Work == |