Supervised learning has been a popular set of machine learning techniques that work effectively in performing regression and classification tasks. However, building supervised learning models require manual data labeling which is slow, expensive and error prone. This slows down model building and limits machine learning applications.
Self-supervised learning, also known as self-supervision, is an emerging solution to these limitations, eliminating the necessity of data labels. By building models autonomously, this technology reduces the cost and time to build machine learning models. It can work without any external interaction and show how humans can come up with certain decisions by their intellect. As there is a still long way ahead of self-supervised learning because technology is newly becoming popular, there aren’t many daily-life applications for now.
In this article, we dive into self-supervised learning and compare self-supervised learning with other machine learning training methods such as unsupervised learning, semi-supervised learning and supervised learning. Along with machine training techniques, we elaborate on self-supervised learning applications/ use cases businesses can apply and what challenges they may face during self-supervised learning adoption.
What is self-supervised learning?
Most learning techniques require training datasets to predict the outcome of test datasets. However, humans need to label those observations in the training datasets manually to achieve proper training datasets that enable AI to understand the dataset and build models for prediction. In situations where the training dataset is too large like image-based data, manual data labeling can take too long.
Self-supervised learning, also known as self-supervision, is an emerging solution to such cases where data labeling is automated, and human interaction is eliminated. In self-supervised learning, the learning 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.
Have we reached peak self-supervised learning?
In the above image, we look for the popularity of both self-supervised learning and self-supervision from Google Trends. Self-supervised learning is the more widely used term compared to self-supervision.
We don’t observe any interest in self-supervised learning before 2016, as self-supervised learning hadn’t been a major area of research back then. After 2016, we see a few minor peaks, and the technology started to gain more interest by 2018. There is still an increasing trend since then. As of September 2020, there are 106 public repositories on Github for Phyton language, and almost 40 of them were updated within 1 month which shows that self-supervised learning topic is emerging among AI talent and upcoming improvements are expected to happen.
However, self-supervised learning is still a new technology, the interest in self supervised learning still represents a tiny amount of the interest in supervised learning as you can see below.
Please note that we have used the forms with hyphen (i.e. “self-supervised”, “self-supervision”). Those without hyphen also include results unrelated to machine learning.
How it differs from supervised/unsupervised learning?
Self-supervised learning vs 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 addition of labels since it generates them by itself.
Self-supervised learning vs semi-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.
Self-supervised learning vs unsupervised learning
Self-supervised learning is similar to unsupervised learning because both techniques work with datasets that don’t have manually added labels. In some sources, self-supervised learning is addressed 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 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.
What are its limitations?
As self-supervised learning is a new technology, we still continue to discover more about it, including its limitations. While the reason behind its limited usage is mostly due to its novelty, we can come up with two possible challenges for self-supervised learning:
Building models can be more 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 other learning methods, self-supervised learning can demand more computing resources.
Inaccurate labels might lead to inaccurate results
You always achieve the best results when you already have labels of your dataset. Self-supervised learning is a solution for when you don’t have any and need to generate them manually. However, this learning 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 while improving self-supervised models.
Why do we need self-supervised learning?
Scalability
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 problematic. Self-supervised learning can automate this process and handle this task with even massive amounts of datas.
Improved AI capabilities
Today, self-supervised learning is mostly used in computer vision for tasks like colorization, 3D rotation, depth completion, or context filling. While these tasks were requiring example labeled cases to build accurate models before, 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 go 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. Yann LeCun, Facebook VP and chief AI scientist 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. While we don’t observe many applications of this technology today, there is a range of use cases where this technology can be applied in the future.
- Colorization: The technology can be used for coloring grayscale images, as seen below.
Source: Perfectial - Context Filling: The technology 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.
Future use cases include:
- Healthcare: This technology 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: Self-supervised learning 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.
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:
- Artificial Intelligence (AI): In-depth Guide
- State of AI technology
- Future of AI according to top AI experts
- Advantages of AI in according to top practitioners
- AI in Business: Guide to Transforming Your Company
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