What is self-supervised learning?

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What is self-supervised learning?

Self-supervised learning is a type of machine learning where the model trains itself on large amounts of unlabeled data to extract features and useful representations without human intervention.

How does it work?

In self-supervised learning, the model takes in unlabeled data and tries to predict or reconstruct missing parts of the dataset or reconstruct parts transformed as a pretext task.

It uses internal representations to predict or reconstruct missing or transformed parts. For example, with a text-based task, the model is trained to predict missing words or the next sentence in a paragraph.

The learned representations after training can be used for downstream tasks, including image classification and language modeling. Since the model has learned to predict or reconstruct missing or transformed data, it can now capture important patterns in the input data.

Here's another representation of a self-supervised learning model for computer vision applications:

Real-world applications of self-supervised learning

  • BERT (Bidirectional Encoder Representations from Transformers)

  • Hate-speech detection at Meta

  • XLM-R (Cross-lingual Language Model - RoBERTa)

  • Google's Medical Imaging Analysis Model

  • ChatGPT (unsupervised pre-training)

Comparison with other types of learning

The table below compares self-supervised, supervised, unsupervised, and semi-supervised learning.

Note: Please scroll horizontally to view the complete contents of the table.

Self-Supervised vs. Supervised vs. Unsupervised vs. Semi-Supervised Learning

Self-supervised learning

Supervised learning

Unsupervised learning

Semi-supervised learning

Description

Machine learning model trained with unlabeled data to create its own labels

Machine learning model trained on labeled data with known outputs

Machine learning model trained on unlabeled data with unknown outputs to identify patterns

Machine learning model trained with unlabeled data and some labeled data with a predefined learning objective

Input Data

Training data used is unlabeled

Training data used is labeled

Training data used is unlabeled

Training data uses lots of unlabeled data and a small mix of labeled data

When to use it

Used for predicting missing pieces of input data

Used to map inputs to outputs

Useful for findng patterns or structures in data

Used to improve accuracy with limited labeled data

Model Building

Used for neural networks, autoencoders, contrastive learning, etc

Used in neural networks, decision trees, random forest models, etc

Used in anomaly detection, data compression, recommender systems, etc

Used in text classifiers, sentiment analysis, etc

Applications

Image and video recognitionNatural Language ProcessingRobotics, etc

Speech recognitionFraud detectionObject detection

Anomaly detectionData compressionRecommender systems

Text classifierSentiment analysis

Algorithms Used

Contrastive Predictive Coding (CPC)SimCLR MoCo

Support Vector Machines (SVMs)Naive Bayes

K-Means ClusteringPCA (Principal Component Analysis)t-SNE (t-distributed Stochastic Neighborhood Embedding)

Expectation-Maximization (EM) AlgorithmLabel Propagation

Originally written for Educative Answers.