A Cookbook of Self-Supervised Learning


Self-supervised learning (SSL) is a machine learning method that allows models to learn from large amounts of unlabeled data without human annotation. This approach has extended deep learning across fields, from language understanding (translation and large language models) to computer vision.

These methods benefit tasks requiring expensive data sets, such as image classification and Natural Language Processing. Furthermore, it also makes an impactful contribution towards downstream tasks, like transfer learning.

1. Getting Started

Self-supervised learning is a machine learning approach that eliminates or minimizes the need to label datasets, saving time and money manually. This approach allows users to train algorithmic models using raw data such as images, videos, or audio clips without relying on labels from human experts.

Utilizing existing and augmented datasets allows you to take full advantage of existing and increased datasets without additional data augmentation, synthetic image or video production, or labeling accuracy issues that could compromise model performance.

SSL (System Learning Learning) has quickly become a powerful new machine learning technique to improve NLP and computer vision algorithms without hand-labeled datasets. Recently, Facebook AI leveraged SSL to rapidly enhance their content understanding systems without depending on labeled datasets to detect hate speech on their platforms, making their products safer for their users and making this leading-edge research more widely applied across their worldwide platform network. Furthermore, SSL underpins cutting-edge foundation models like GANs, VACs, transformer models, and many others that advance cutting-edge foundation models such as generative adversarial networks (GANs), variance autoencoders, and VACs transformer models.

2. Data Preparation

Data preparation, also called data wrangling, is one of the critical tasks associated with machine learning and one of the more challenging parts of being a data analyst.

BI, analytics, and data science teams play an integral part in this work as they integrate new and existing internal systems with external sources of information for use in business analysis and machine learning models. Some estimates indicate that up to 80% of an analyst’s time is dedicated to finding, cleaning, and preparing data rather than performing business analyses.

Data preparation makes identifying and correcting errors easier, from simple tasks such as ensuring all numeric values are stored in native formats to more complex processes like normalizing data from different sources, merging datasets, or enriching information with additional attributes. Talend’s self-service data preparation tools simplify these processes with user-friendly visual recipes, making them accessible even to non-technical users.

3. Training

Supervised learning involves training a model on data containing high-quality manual labels to fine-tune its weights to achieve accurate classification, regression, and other tasks. Unfortunately, providing this model with high-quality tags at once is sometimes impractical or impossible.

Self-supervised learning can train a model with unlabeled data and automatically label its outputs. This process is commonly known as predictive or pretext learning and involves several approaches; one common one includes employing a language model to predict what word comes next based on context.

SSL can also be found in everyday applications, like Instagram reels that allow you to rotate a 2D image into 3D style or Android phones that recognize and unlock automatically using facial recognition technology. SSL is also used in video editing, transformation, and even natural language processing functions like text sequence prediction or auto-encoding models like Word2Vec (see image below).

4. Testing

Self-supervised learning differs significantly from supervised learning by not relying on high-quality labeled data for its training process; instead, models can produce their labels while being trained using the inherent structures within data – saving engineers, data scientists, and data operations teams time and effort in label creation processes.

Self-supervised learning methods also deliver improved generalization, meaning a model is more likely to make accurate predictions on unseen data, thanks to their algorithm’s ability to recognize patterns across various forms of the same input data.

There is an array of self-supervised learning techniques available today. Autoencoder models train a neural network to encode what it sees into low-dimensional representations that it can later decode back into original data forms, while contrastive coding models present two versions of identical data and measure whether their compatibility matches up; then use that information to improve output quality by detecting subtler differences like color or shade differences.

5. Optimization

Optimization is a mathematical framework and technique to solve quantitative problems across disciplines. The aim is to reach maximum or minimum performance in any specific objective using real-world variables, such as increasing investment returns, decreasing production costs, or even something as straightforward as determining whether signatures on documents have been falsified.

Collecting enough labeled data can be costly and time-consuming, making self-supervised learning an effective solution to reduce costs and timelines by training models with less marked information.

Optimizing parameters and hyperparameters is often an integral component of self-supervised learning workflows; for instance, Facebook utilizes this technique – known as XLM – to quickly train language systems without human input, improve hate speech detection products on its platforms, and train its systems without human interference.