What is Pre-Text Data in Self-Supervised Learning?

In the context of self-supervised learning, a pretext task is a task that a model is trained on without the use of labeled data, in order to learn useful representations of the data. The pretext task is designed to provide a task-specific objective that the model can use to learn useful representations of the input data.

For example, in the case of self-supervised learning for natural language processing, a common pretext task is to mask out a word in a sentence and ask the model to predict the missing word based on the context of the other words in the sentence. By training on this pretext task, the model learns to understand the context in which words appear, which can be useful for a variety of downstream tasks, such as text classification or sentiment analysis.

Another example of a pretext task for self-supervised learning in computer vision is to ask the model to predict the rotation angle of an image. By training on this pretext task, the model learns to recognize and extract useful features from the image, such as edges and corners, that can be used for other tasks, such as image classification.

The key idea behind self-supervised learning and pretext tasks is to provide the model with a task that is related to the downstream task of interest but does not require labelled data. By doing so, the model can learn to extract useful features and representations of the data that can be transferred to other tasks, without the need for expensive manual labeling.

Let’s understand this with an example.

Let’s say we have a large dataset of text data, but we don’t have any labelled examples for a specific task we want to perform, such as sentiment analysis. Instead of manually labelling the data, we can use a pretext task to train a self-supervised model on the data. One common pretext task is called masked language modelling. In this task, we randomly mask out a word in a sentence and ask the model to predict the missing word based on the context of the other words in the sentence. For example, given the sentence:

“The baby is playing with the ___”

We might mask out the word “ball” and ask the model to predict the missing word based on the context of the other words in the sentence.

By training on this pretext task, the model learns to understand the context in which words appear and can extract useful features from the data that can be used for other downstream tasks, such as sentiment analysis. In this way, the model can be trained without the need for manually labelled data, which can be time-consuming and expensive to obtain.

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