A SELF-SUPERVISED DEEP LEARNING AND ITS’ APPLICATION A SYSTEMATIC REVIEW

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dc.contributor.authorMOAAZ ZAIGHUM MSIT
dc.date.accessioned2025-09-27T13:30:01Z
dc.date.available2025-09-27T13:30:01Z
dc.date.issued2021
dc.description.abstractUnsupervised Learning based on unlabeled data from large-scale or any human-an noted labels. But the self supervised learning emulate the way used by humans to classify data. In the thesis, the various schema and evaluation metrics of self-supervised learning techniques are examined, followed by a study of the most widely utilized datasets such as photos, videos, audios, and 3D data and the presently available self-supervised visual feature learning methods. For both image and video feature learning, quantitative performance comparisons of the examined algorithms on benchmark datasets are shown and discussed at the beginning of this portion of the thesis. Finally, this work concludes with a list of prospective future avenues for self-supervised visual feature learning that has been found. Also, a Survey of some of the most useful Self-supervised apps is discussed.
dc.identifier.urihttps://escholar.umt.edu.pk/handle/123456789/7249
dc.language.isoen
dc.publisherUMT, Lahore
dc.titleA SELF-SUPERVISED DEEP LEARNING AND ITS’ APPLICATION A SYSTEMATIC REVIEW
dc.typeThesis
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