Self-Supervised Learning
Self-supervised learning is a type of machine learning in which a model is trained using a pre-text task that does not require labelled data. Instead, the model learns to solve the task by making use of the inherent structure or patterns in the data itself.
In self-supervised learning, the model is presented with a set of input data, such as images or text, and is asked to predict some aspect of the data. For example, in the case of text data, the model might be presented with a sentence with a missing word and asked to predict the missing word based on the context of the surrounding words. In the case of image data, the model might be asked to predict the rotation angle of a rotated image.
By training on these pre-text tasks, the model can learn to extract meaningful features from the data that can be used for other downstream tasks, such as image classification or language translation. Since self-supervised learning does not require labelled data, it can be used to train models on large datasets without the need for manual annotation, which can be expensive and time-consuming.
Self-supervised learning has become an increasingly popular approach in the field of deep learning, particularly in natural language processing (NLP) and computer vision, where it has been used to achieve state-of-the-art results on a variety of benchmarks.
Difference between Unsupervised & Self-Supervised Learning
Self-supervised learning and unsupervised learning are two types of machine learning approaches that differ in the way they learn from data.
In unsupervised learning, the model is trained on a dataset that has no predefined labels or categories. The goal is to discover the underlying structure or patterns in the data without any explicit guidance. For example, in the case of clustering, the model is asked to group similar data points together based on some similarity metric. In the case of dimensionality reduction, the goal is to reduce the number of features in the data while preserving the most important information.
In contrast, self-supervised learning is a type of supervised learning in which the model is trained on a pretext task that does not require labelled data. Instead, the model learns to predict some aspect of the data, such as the missing word in a sentence, by making use of the structure or patterns in the data itself. The model is then fine-tuned on a downstream task that does have labelled data, such as classification or regression.
The main difference between self-supervised learning and unsupervised learning is that self-supervised learning uses a pretext task to guide the learning process, whereas unsupervised learning does not. Self-supervised learning can be seen as a way to provide supervision without requiring explicit labels, while unsupervised learning is more focused on discovering the underlying structure of the data.
Both self-supervised learning and unsupervised learning have their own strengths and weaknesses, and the choice of approach will depend on the specific requirements of the problem at hand.
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