Self-supervised learning is a means for training computers to do tasks without humans providing labeled data (i.e., a picture of a dog accompanied by the label “dog”). It is a subset of unsupervised learning where outputs or goals are derived by machines that label, categorize, and analyze information on their own then draw conclusions based on connections and correlations.
Self-supervised learning can also be an autonomous form of supervised learning because it does not require human input in the form of data labeling. In contrast to unsupervised learning, self-supervised learning does not focus on clustering and grouping that is commonly associated with unsupervised learning.
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Most machine learning (ML) algorithms are bogged down by massive amounts of data that need processing. Self-supervised learning is an attempt to create a data-efficient artificial intelligence (AI) system.
How Useful is Self-Supervised Learning?
The concept of self-supervised learning aims to address challenges in supervised learning when it comes to collecting, handling, cleaning, labeling, and analyzing data. Developers who want to create an image classification algorithm, therefore, create supervised learning-capable systems to collect comprehensive data to get a representative sample. Apart from feeding the computer image datasets, developers need to classify the images before they can be used for training. The process is arduous and time-consuming compared with how humans approach learning.
The human learning process is multifaceted. It involves both supervised and unsupervised learning processes. While we learn via experiments and curiosity, we also acquire knowledge better using fewer and simplified data. Even now, this remains a challenge for deep learning systems. While we have seen advances in learning-based AI systems that can break down speech, images, and text, performing complex tasks remains a challenge for these. That is what self-supervised learning is trying to address.
In short, self-supervised learning allows AI systems to break down complex tasks into simple ones to arrive at a desired output despite the lack of labeled datasets.
How does Self-Supervision Work?
The basic concept of self-supervision relies on encoding an object successfully. A computer capable of self-supervision must know the different parts of any object so it can recognize it from any angle. Only then can it classify the thing correctly and provide context for analysis to come up with the desired output.
Real-World Applications of Self-Supervised Learning
According to Yann Lecun, a computer scientist known for his impressive work in the ML field, the closest we have to self-supervised learning systems are the so-called “Transformers.” These are ML models that successfully use natural language processing (NLP) without the need for labeled datasets. They are capable of processing massive amounts of unstructured data and “transform” them into usable information for various purposes. The Transformers are behind Google’s BERT and Meena, OpenAI’s GPT2, and Facebook’s RoBERTa. But while they are better than their predecessors at answering questions, they still require much work to hone their understanding of human linguistics.
Aside from processing unstructured data, the Transformers can also solve problems that involve manipulating symbols, which makes them useful in developing neural networks that carry out pattern recognition and statistical estimation.
To date, the Transformers are vital in processing words and mathematical symbols easily. However, translating them into visual representations remains a challenge.
Self-supervised learning is proving to be a significant component of AI and ML that would help experts resolve today’s pressing challenges.
