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Abstract

Machine learning has made significant progress in recent years, resulting in the development of an impressive array of algorithms that enable us to perform a wide variety of tasks. According to the literature, most algorithms rely on supervised learning. However, despite achieving good results in different tasks, the main drawback of this learning paradigm is its dependency on manually created human labels, as the labeling process is very costly. Moreover, machine learning models tend to learn shortcuts that promote incorrect biases, leading to failures in the tasks they aim to accomplish. To avoid these issues, self-supervised learning has recently gained attention as a learning paradigm. This work provides an introduction to the literature on self-supervised learning. It explains the different types of methods distinguished within this learning paradigm and examine the basic procedures for applying them to different types of data.