Self-Supervised Learning (SSL) is an emerging research topic that aims to solve the challenges posed by the dependency on labelled data when building AI models. To grasp the significance of self-supervised learning, it is essential to establish a foundational understanding of machine learning (ML) techniques and the pivotal role that labelled data plays within these ML techniques.
Supervised Learning
Supervised learning is a fundamental and widely used approach in machine learning and has applications in various domains, including natural language processing, computer vision, healthcare, finance, and many others. The availability of labelled training data is a critical factor in the success of supervised learning algorithms.
Labelled Data
Labelled data is a critical resource in machine learning. Also known as annotated data, it refers to data in which each individual data piece or example is paired with one or more labels or annotations that describe its characteristics, properties, or the correct outcome associated with it (ref ‘A’ in diagram). These labels serve as ground truth or reference points for machine learning algorithms during the training process. In supervised learning, an AI algorithm is provided with a set of labelled data (ref ‘B’ in diagram), meaning that each input is associated with the correct output or target. The goal of supervised learning is to learn a mapping or function that can make accurate inferences or classifications on new data (ref ‘C’ in diagram).
Supervised learning has a proven track record for producing specialist models that perform extremely well on specific task these models were trained to do. However, supervised learning is bottlenecked by the need for labelling when it comes to building larger foundational models that can do multiple tasks.
UnSupervised Learning
Unsupervised learning is a type of machine learning where the algorithm is trained on data without explicit supervision or labelled outcomes. Unlike supervised learning, where the algorithm is provided with labelled data to learn a mapping between inputs and outputs, unsupervised learning involves extracting patterns, relationships, or structures directly from the input data. Unsupervised learning is valuable when dealing with large datasets where labelling every data point is impractical or too costly. It is a powerful approach for discovering patterns and insights within the data itself, allowing for exploratory analysis and data-driven decision-making.
Self Supervised Learning
Self-supervised learning is a subset of unsupervised learning. It is a machine learning paradigm where a model learns from the data itself without requiring external labelled annotations. Though similar in approach to supervised learning, where labelling of data is required, in self-supervised learning algorithms creating their own labels from the data itself. For that reason, self-supervised learning can be considered as a branch of unsupervised learning since there is no manual labelling involved.
Common sense forms the bulk of intelligence in humans and animals. It helps humans learn new skills without requiring teaching for every single task a human being is presented with in life. For example, children from a very early age are able to recognise a cat they see in the park only having been introduced to the pet cat at home. In comparison training an AI system to do something similar using supervised learning will require numerous examples of cat images. It still might fail to recognise a Maine Coon perched on a tree.
Human beings are able to accomplish this feat by relying on previously acquired background knowledge. Self-supervised learning is one technique that enables AI models to build such background knowledge. A self-supervised learning model learns in two steps. In the first stage (see Pretext task in diagram), by using various methods and learning techniques, self-supervised models create background knowledge (in the form of labels and annotations). In the second stage (see downstream tasks in diagram), the actual training task is performed with supervised or unsupervised learning. This is akin to how human beings learn – largely by observation.
Conclusion
Self-supervised learning is an emerging area of research that is making a splash on the AI arena and gaining attention because it brings of its innovative approach, where AI systems learn to interpret data without explicit labels. Self-supervised learning could be a game-changer. In summary, the future of self-supervised learning appears promising. As research continue, we can anticipate its increasing integration into various AI applications and domains. Self-Supervised learning is likely to be at the forefront of advancements in building more intelligent, adaptable, and data-efficient AI systems.
