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Self-Supervised Learning: Benefits & Uses in 2024

Supervised learning has been a popular set of machine learning techniques that work effectively in performing regression and classification tasks. However, supervised learning models require manual data labeling which slows down the model building process, is expensive, and error prone.  

Self-supervised learning (SSL), also known as self-supervision, is an emerging solution to the challenge posed by data labeling. By building models autonomously, self-supervised learning reduces the cost and time to build machine learning models. In this article, we dive into self-supervised learning and compare it with other machine learning approaches such as supervised and unsupervised learning.

What is self-supervised learning?

Self-supervised learning is a machine learning approach where the model trains itself by leveraging one part of the data to predict the other part and generate labels accurately. In the end, this learning method converts an unsupervised learning problem into a supervised one. Below is an example of a self-supervised learning output.

Input and output of self-supervised learning example
Source: Arxiv

Why is self-supervised learning important now?

Most machine learning techniques require training datasets to make predictions. Data scientists need to label the observations in the training datasets manually or with data labeling tools to enable AI to understand the input data and make accurate predictions about new data. In cases where the training dataset is too large, manually labeling training data can be quite costly and time-consuming.

Self-supervised learning eliminates the necessity of data labeling. It enables computers to label, categorize, and analyze data themselves.

What is the level of interest in self-supervised learning?

As seen in the graph below, there is a steady increase in the level of interest in self-supervised learning since researchers from Google introduced the BERT model at the end of 2018 which leverages self-supervised learning for natural language processing (NLP) tasks.

The interest on self-supervised learning is increasing.

Source: Google Trends

Since AI/ML models require huge datasets and labeling this data is one of the biggest challenges of machine learning adoption, we expect this trend to continue.

What are its differences from supervised/unsupervised learning?

Supervised learning vs self-supervised learning

The common characteristic of supervised and self-supervised learning is that both methods build learning models from training datasets with their labels. However, self-supervised learning doesn’t require manual labeling since it generates them by itself. 

Semi-supervised learning vs self-supervised learning

Semi-supervised learning uses manually labeled training data for supervised learning and unsupervised learning approaches for unlabeled data to generate a model that leverages existing labels but builds a model that can make predictions beyond the labeled data. Self-supervised learning relies completely on data that lacks manually generated labels.

Unsupervised learning vs self-supervised learning

Self-supervised learning is similar to unsupervised learning because both techniques work with datasets that don’t have manually added labels. Accordingly, self-supervised learning can be considered as a subset of unsupervised learning. However, unsupervised learning concentrates on clustering, grouping, and dimensionality reduction, while self-supervised learning aims to draw conclusions for regression and classification tasks.

Hybrid approaches vs self-supervised learning

There are also hybrid approaches that combine automated data labeling tools with supervised learning. In such methods, computers can label data points that are easier to label by relying on their training data and leave the complex ones to humans. Or, they can label all data points automatically but need human approval. In self-supervised learning, automated data labeling is embedded in the training model. The dataset is labeled as part of the learning processes; thus, it doesn’t ask for human approval or only label the simple data points. 

Why do we need self-supervised learning?


Supervised learning requires labeled data to predict outcomes for unknown data. However, it can need large datasets to build proper models and make accurate predictions. For large training datasets, manual data labeling can be challenging. Self-supervised learning can automate this process and handle this task with even massive amounts of data.

Improved AI capabilities

Today, self-supervised learning is mostly used in computer vision for tasks like colorization, 3D rotation, depth completion, or context filling. These tasks require example labeled cases to build accurate models but self-supervised learning can improve computer vision or speech recognition technologies by eliminating the necessity of example cases.

Understanding how the human mind works

Supervised models require human intervention to perform appropriately. However, those interventions don’t always exist. Then, we can think of introducing reinforcement learning to machines to make them start from the beginning in cases where they can get immediate feedback without negative consequences. However, this does not cover many real-world scenarios. Humans can think through the consequences of their actions before making them, and they don’t have to experience all actions to decide on what to do. Machines also have the potential to work in the same way.

Self-supervised learning steps in at this point. It automatically generates labels without human intervention and enables machines to come up with a solution without any interference. Facebook VP and chief AI scientist Yann LeCun shares that self-supervised learning is a step towards how human intelligence works. As we understand this better, we will get closer to create models that think more similar to humans.

What are its applications?

Self-supervised learning technologies mostly focus on improving computer vision and natural language processing (NLP) capabilities.

  • Colorization: SSL can be used for coloring grayscale images, as seen below.
Colorization of images with self-supervised learning
Source: Perfectial

Context Filling: SSL can fill a space in an image or predict a gap in a voice recording or a text. Video Motion Prediction: Self-supervised learning can provide a distribution of all possible video frames after a specific frame.

Other use cases include:

  • Healthcare: Self-supervised learning can help robotic surgeries perform better by estimating dense depth in the human body. It can also provide better medical visuals with improved computer vision technologies such as colorization and context filling.
  • Autonomous driving: SSL can be used in estimating the roughness of the terrain. It can also be useful for depth completion to identify the distance to the other cars, people, or other objects while driving.
  • Chatbots: Self-supervised systems can also be applied to chatbots. Transformers, a chatbot that leverages self-supervised learning, is successful in processing words and mathematical symbols easily. However, it is still far from understanding human language.

What are its limitations?

  • Can be computationally intense: Learning models with labels can be built much faster compared to unlabeled learning models. Plus, self-supervised learning autonomously generates labels for the given dataset, which is an additional task. Therefore, compared to supervised learning methods, self-supervised learning can demand more computing power.
  • Labeling accuracy: You always achieve the best results when you already have labels for your dataset. Self-supervised learning is a solution for when you don’t have any and need to generate them manually. However, the model can come up with inaccurate labels while processing and those inaccuracies can lead to inaccurate results for your task. Thus, labeling accuracy is an additional factor to consider about self-supervised models.

To learn more on self-supervised learning

Yann LeCun, VP and Chief AI Scientist at Facebook, is explaining how self-supervised learning works. You can watch the video of his lesson at New York University to learn more about the technical details of this approach:

Here is a list of more AI-related articles you might be interested in:

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Cem Dilmegani
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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Surekha Bhanot
Jul 30, 2021 at 14:26

Very interesting, clear crisp

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