When to use self supervised learning

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Are you wondering what self supervised learning is? Or maybe you want to learn more about when self supervised learning should be used? Well either way, you are in the right place! In this article, we tell you everything you need to know to understand when self supervised learning should be used.

We start out by providing information on what self supervised learning is and how it works. After that, we discuss what type of data you need to have available in order to use self supervised learning techniques. After that, we discuss some of the main advantages and disadvantages of using self supervised learning. This provides context for an eventual conversation on when self supervised learning should and should not be used.

What is self supervised learning?

What is self supervised learning? Self supervised learning is a model training scheme that makes it possible to train supervised machine learning models on unsupervised data. The general idea behind self supervised learning is that you can use natural relationships or structure that exists in unlabeled data to create labels or signals that can be used to train a machine learning model. These natural labels can then be used to train supervised machine learning models.

We will provide a few examples of what self supervised learning looks like to make the concept feel more concrete. It is most common for self supervised learning to be used when dealing with text or image data, so we will provide examples from these domains. In the text domain, it is common to create natural labels from unlabeled data by feeding a model a list of words and asking it to predict the next word that will appear. In this example, the string of words that appear before the masked work become the features or inputs for the supervised model. The masked word becomes the output for the supervised model. In the image domain, it is common to mask a small patch of an image then ask a model to predict what that patch looks like given the surrounding context.

We will note that while this is not always the case, self supervised learning is often used in conjunction with more traditional supervised learning paradigms. It is common for models to undergo a few rounds of self supervised training on unlabeled data, then later be put up to a few rounds of supervised training on a specific task. This type of paradigm is often seen in transfer learning.

What data is needed for self supervised learning?

What data is required in order to be able to use self supervised learning? One of the main advantages of self supervised learning is that it enables you to train supervised machine learning models on unlabeled data. That means that you do not need any labeled data in order to train a self-supervised model. All you need is a large batch of unsupervised data that has some natural structure that you can take advantage of in order to create natural labels for the data.

Advantages and disadvantages of self supervised learning

What are the main advantages and disadvantages of self supervised learning? In this section, we will discuss some of the main advantages and disadvantages associated with self supervised learning. In particular, we will focus on advantages and disadvantages that show how self supervised learning compares to other model training schemes like weakly supervised learning and semi supervised learning.

Advantages of self supervised learning

Here are some of the main advantages of self supervised learning.

  • Requires no labeled data. The main advantage of self supervised learning is that it does not require any labeled training data. That means that you can train a supervised model on large batches of data without having to use any resources to label that data.
  • Increased accuracy when used in conjunction with supervised learning. Another advantage of self supervised learning is that you can often improve the accuracy of a traditional supervised model by pre-training it with a few rounds of self supervised training. This is especially true if it enables you to train the model on more data overall.
  • More cost effective. If you are in a situation where obtaining high quality labels for your data is time and resource intensive, then using techniques that can accommodate less than perfect labeled data will generally be more cost effective.

Disadvantages of self supervised learning

Here are some of the main disadvantages of self supervised learning.

  • Requires natural relationships or structure in data. One potential disadvantage of self supervised learning is that it requires there to be some type of structure or relationships in the data that can be used to create natural labels for training. There are generally some relationships that can be taken care of, but it is possible that there are some types of datasets where such relationships do not exist.
  • Requires care when generating natural labels. Another disadvantage of self supervised learning is that it often requires a good deal of care and experimentation to make sure that you are generating natural labels in a way that makes sense for your use case. If you use the wrong method to generate labels for your unlabeled data, it may have detrimental effects on your model.
  • Computational inefficiency. Since creating the labels for your data is part of the self supervised learning process, there are more steps required in self supervised learning paradigms than other model training paradigms. That means that self supervised learning pipelines generally require more computational resources than paradigms where you do not need to employ one or more steps to generate labels for your data.
  • Decreased accuracy when used on its own. Models that are trained only using self supervised learning generally have lower accuracy than models that are trained using a standard supervised learning paradigm. This is part of the reason that it is common to pre-train a model using a self supervised paradigm then use labeled data and a supervised learning paradigm to finish the model off at the end.
  • Higher complexity. Self supervised learning is more complex and less common than standard supervised learning techniques. That means it will be more difficult to get teammates up to speed and find people who can give you meaningful feedback on your work.

When to use self supervised learning

When does it make sense to use self supervised learning instead of another model training paradigm like weakly supervised learning? In this section, we will provide examples of situations where it makes sense to use self supervised learning.

  • When you do not have any labels for your data. In general, it makes the most sense to use self supervised learning when you have a large amount of data that you need to label and it is difficult to obtain labels for that data. This might happen, for example, when you want to train a large model that requires a lot of data, such as a large neural network. If there is no existing data source that you can use to create labels for a supervised learning model, it can be very time consuming to manually label data.

When not to use self supervised learning

When does it not make sense to use self supervised learning? In this section, we will provide examples of situations where it makes sense to use self supervised learning.

  • When labeled data is easy to obtain. Sometimes there are already labels that are available to be used for your data. Other times labels are not directly available, but it is easy to obtain labels. If you are in a situation where it is easy to obtain labels for your data, then it generally makes sense to use a standard supervised learning model. Supervised models will generally have better performance than self supervised models that were trained on the same amount of data.

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