CRITICAL ANALYSIS OF SELF-SUPERVISION: Difference between revisions
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== Introduction == | == Introduction == | ||
This paper evaluated the performance of state-of-the-art unsupervised learning methods on learning weights of convolutional neural networks (CNNs) to figure out whether self-supervised methods can learn deep features from only one image. | |||
In self-supervised learning, data generate ground truth labels per se by pretext tasks such as rotation estimation. The main goal of self-supervised learning is utilizing unlabeled data, e.g., a picture of a dog without the label “dog”, for training CNNs and finding generalized image representations. | |||
== Previous Work == | == Previous Work == | ||
Revision as of 22:01, 25 November 2020
Presented by
Maral Rasoolijaberi
Introduction
This paper evaluated the performance of state-of-the-art unsupervised learning methods on learning weights of convolutional neural networks (CNNs) to figure out whether self-supervised methods can learn deep features from only one image. In self-supervised learning, data generate ground truth labels per se by pretext tasks such as rotation estimation. The main goal of self-supervised learning is utilizing unlabeled data, e.g., a picture of a dog without the label “dog”, for training CNNs and finding generalized image representations.