Self Supervised Learning - Types, Examples and Applications

Have you ever wondered how Instagram creates 3D filters? How does Snapchat rotate the original photo of you in 3D mode? How does your android phone unlock after having your presence on the screen, or how can your device be so familiar to your voice? Interesting questions, right? 

 

(Related blog - How Instagram uses AI and Big Data)

 

Well, these all are not some God’s miracle, but the applications of self-supervised learning (SSL). Let us understand SSL and other related terms step by step.

 

 

Machine Learning and Self Supervised Machine Learning

 

Machine Learning

 

Machine learning is an art of artificial intelligence. The main task in machine learning is understanding computer algorithms. How does a computer work? Or how does a computer process? 

 

This understanding of algorithms comes with experience and hands on data. Machine learning is usually applied to tasks such as filtering of emails, medicine generation, and computer vision.

 

Real life examples of Machine Learning

 

Machine learning has many applications. Here are those application from real life:

 

1. Medical Diagnosis: Medical Diagnosis is one of the most important and useful applications of machine learning. Various checkups such as x-ray, CT scans, etc. are its principal application.

 

2. Face recognition: Only if you ever noticed how your phone unlocks in a fraction of seconds, after noticing your face on screen. This is also an application of machine learning.

 

3. Voice recognition: The voice application of Google in your android, or Siri of your iPhone is familiar with your voice. This voice identification is also an application of machine learning.

 

Self-supervised Learning

 

SSL expands for Self-supervised learning. It is also known as self-supervision. It is one of the most common methods of machine learning. In simple words, SSL may be defined as an intermediate between supervised and unsupervised learning. It is a feature of neural networks.

 

What is the difference between supervised and unsupervised learning?

 

Before moving to the working of Self-supervised learning, let us understand the difference between supervised and unsupervised learning mentioned above:

 

1. Supervised learning : Supervised learning may be defined as a labeled training. In this type of learning, a roadmap of the tasks or its algorithm is already provided to the users.

 

2. Unsupervised learning : In simple language, unsupervised learning is a non-labeled training. The algorithms of the tasks of the computer remain unknown to the users. 

 

And SSL is an intermediate of these two. It eliminates human interaction while developing labels. Self-supervised learning itself draws the label.

 

(Suggested blog: Classification of algorithms)

 

Recently, the principles of self-supervised learning are applied in Facebook voice search feature.

 

 

How does Self-supervised learning work?

 

Self-supervised learning is a part of neural learning. And any kind of neural learning works with the following process.

 

1. The algorithm given is solved on pseudo-labels. This step helps in initializing the network.

 

2. After the network is initialized the algorithm or task is performed in supervised or unsupervised learning mode.

 

In this way, self-supervised learning works. It tries to emulate the way in which humans classify objects.

 

 

Types of data used in self-supervised learning

 

Any machine learning including self-supervised learning involves two types of data. 

 

1. Positive data: Data which is the objective of the algorithm.

 

2. Negative data: Data which is nonessential in the eye of an algorithm.

 

For example, the algorithm has a task to identify the traffic signs in an image grid. All the traffic signs shown in it are positive data, while the rest of images are the negative data. This example might have brought ‘prove that you are not a robot’ question in your mind. That selection game is itself an example of SSL.

 

(Suggested blog: AI in machine learning)

 

 

What are the types of self-supervised learning?

 

Self-supervised learning is classified into two major types, Contrastive and Non-contrastive. Here is the brief description of both the types.

 

1. Contrastive self-supervised learning

 

Contrastive learning methods use both types of data i.e., positive and negative data. There are two main functions of this learning.

 

• To reduce the distance between positive data to minimum.

 

• To increase the distance between negative data to maximum.

 

2. Non-contrastive self-supervised learning

 

Non-contrastive learning methods use only positive data. This learning congregates on a useful local minimum. It doesn’t focus on reaching the expected identity function with zero loss.

 

What are the applications of self-supervised learning?

 

Self-supervised learning has its soul in almost every existing technology. Following are the major applications of self-supervised learning:

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Top 10 applications of Self-supervised Learning

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1. Face Detection

 

This is one of the most common applications of SSL. Self-supervised learning is used in matching on screen faces with the input fed data. It is used for security purposes in mobile phones.

 

2. Signature Acknowledgment

 

Self-supervised learning is used in recognizing signatures. This application is used to verify user credentials from different documents in online mode.

 

3. Colorization

 

Self-supervised learning helps in imparting different colors to a particular image. Or, in layman’s terms, filters you use in your photos like grayscale, vintage effect, etc. are the perks of SSL.

 

4. Text Categorization

 

Self-supervised learning categorizes the text according to specific needs. This application is used in presenting a document online.

 

5. Weather Forecasting

 

Weather forecasting is a complex process. Using supervised or unsupervised data can affect the accuracy of a report. In such cases, self-supervised learning turns out as a savior. It helps in creating climatic reports with accuracy and precision.

 

6. Context Filling

 

Self-supervised learning is applied in context filling. SSL is used to fill the spaces in an image or a gap in an audio.

 

7. 3D Rotation

 

If you are a frequent Instagram reel user, you might be aware of 3D filters in reels, which help your original 2D image to rotate in 3D style. That’s too an example of self-supervised learning.

 

8. Spam Discovery

 

Self-supervised learning detects the presence of spam in your newsletters, phone call list, emails, and other messages.

 

9. Depth Accomplishment

 

Self-supervised learning helps the user to deepen the color or shade in any photo or picture. This application of SSL is used by editors around the globe.

 

10. Customer Insights

 

If you own a business, you might be still searching for the benefits of SSL in your field. For you SSL can act as a searching tool. Self-supervised learning helps in searching the customers of your interest around the globe.

 

11. Stock Price Predictions

 

SSL also helps in stock price predictions.

 

 

What are the top examples of self-supervised learning?

 

Now when the applications of the self-supervised learning are clear. Let us consider the top two real-life examples of self-supervised learning.

 

1. Wav2vec

 

It is a self-supervised algorithm of Facebook. It helps in performing speech recognition.

 

2. BERT

 

BERT stands for Bidirectional Encoder Representations from Transformers.  It is Google's algorithm that helps to understand search queries better.

 

What are the advantages of self-supervised learning?

 

Self-supervised learning (SSL) has following advantages:

 

1. SSL can also operate in lower quality of data.

 

2. SSL is more closely related to the human way of classifying things.

 

3. SSL makes the use of data that lacks manually generated labels.

 

4. Self-supervised learning draws the most accurate and precise conclusions.

 

5. The SSL model doesn’t require any manual addition of labels.

 

6. SSL labels the simple data points.

 

7. SSL is faster in process than manual labeling.

 

8. SSL has more improved AI capabilities than other machine learning.

 

9. SSL can even help to predict the conclusion related to unknown data.

 

10. SSL understands the human mind better, and thus, helps in building real-world scenarios.

 

 

Limitations of self-supervised learning

 

Self-supervised learning has few limitations such as,

 

1. It takes time to build unlabeled models.

 

2. Inaccuracy in labeling, can cause errors in the results.

 

Self-supervised learning is an emerging technology. It is useful in every phase of life. It has its application in healthcare, editing, forecasting, driving, chatbots, video editing, transforming, and data recognition. Few limitations are needed to concern yet once these barriers are crossed SSL can be a new benchmark in the field of technology.