CRITICAL ANALYSIS OF SELF-SUPERVISION: Difference between revisions
Jump to navigation
Jump to search
| Line 3: | Line 3: | ||
== Introduction == | == Introduction == | ||
This paper evaluated the performance of state-of-the-art self-supervision techniques on learning different parts of convolutional neural networks (CNNs). | |||
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. | |||
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. | |||
== Previous Work == | == Previous Work == | ||
Revision as of 10:44, 25 November 2020
Presented by
Maral Rasoolijaberi
Introduction
This paper evaluated the performance of state-of-the-art self-supervision techniques on learning different parts of convolutional neural networks (CNNs). 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.
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.