Self-Supervised Learning (SSL)

By: Anina Ot | Reviewed by Kuntal ChakrabortyCheckmark | Last updated: April 15, 2021

What Does Self-Supervised Learning (SSL) Mean?

Self-supervised learning is a machine learning approach that does not rely on the human element to label and categorize training objects. Instead, the machine would label, categorize, and analyze various sets of data to reach conclusions independently from outside influence.

Due to the lack of human assistance throughout the learning process, this approach requires powerful and complex machine learning algorithms along with high computational power. They need to be able to handle massive amounts of data of various types and be able to catalog and categorize them flexibly and effectively.

As a type of unsupervised learning, self-supervised learning is often used to train Artificial Intelligence (AI) systems using large sets of data, where labeling items one by one, would be time-consuming and inefficient. However, even the most capable self-supervised learning algorithm cannot extract something out of nothing.

Proper encoding of all training items is key to a successful self-supervised learning approach. The more detailed and data-rich each study item is, the more information the AI system can successfully extract from them. As a result, the system would have a better chance at classifying items and input correctly in relation to other items during and after concluding training.

Because self-supervised learning uses previously-learned information to predict data patterns and upcoming events, effectively becoming smarter, it is not limited by human capabilities. Its independence allows it to be highly scalable, growing exponentially into pattern prediction and recognition skills, along with advanced decision-making capabilities.


Techopedia Explains Self-Supervised Learning (SSL)

In general, AI systems that are designed using self-supervised learning are not used to directly solve a problem in the data it was first presented with. With this approach, the system creates clusters of data points that show a set of similarities or share patterns, while being as different as possible from other clusters. As a result, the AI system would provide information on how it represented the objects it analyzed. The representation it figured out, or the simple neural network, would come in handy in solving similar object-oriented tasks in the future.

While not immaculate, self-supervised machine learning can open many doors when it comes to developing AI systems and deep learning. Some benefits that are unique to self-supervised learning include:

  • Scalability – Without self-supervised learning, building strong prediction and categorization models would be inefficient and time-consuming. Alternatively, AI systems that rely on self-supervised learning can automate sets of complex tasks as long as they have adequate computational power, knowledge, and time.

  • Efficient Problem Solving – No longer needing to rely on the preconceived notions of the human brain with labeled data, AI systems on their own can find the best route to solving a problem, from filling gaps in images to statistical predictions and object categorization.

  • Improving Computer Vision – Self-supervised learning lets AI systems train themselves similar to how a human brain grows to recognize its surrounding environment. It ensures the system does not get stuck or waste computational power looking for similarities between what it is seeing and already labeled training items.

  • Recreating Human Intelligence – Similarly to improving computer vision, AI systems that rely on self-supervised learning not only have the potential to grow to near-human levels of intelligence but can also help neuroscientists understand how the human brain works.

With all its benefits, the self-supervised machine learning approach has limitations that prevent it from wide-spread use. For one, it requires enormous computational power that is hard to come by for smaller projects and amateur developers. Additionally, self-supervised learning, by default, is highly sensitive. Small inaccuracies in the items used to train it or how they were encoded could yield highly inaccurate results that are near-impossible to fix or ‘debug’ individually.

Additionally, work is being done to with images, specifically using the SimCLR framework, and advances with natural language processing (NLP) are impacting the field of self-supervised learning in ways that are exciting to those in the industry, with great benefits to consumers and end-users.

Still, with all of its limitations and relative infancy, self-supervised learning is what many computer scientists hope for the future. During the Association for the Advancement of Artificial Intelligence (AAAI) 2020 conference, the French computer scientist, Yann LeCun, said that self-supervised learning is what would take AI and deep learning systems to the next level.


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