Active Learning in Machine Learning: Everything You Need to Know

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David Horvath


The modern world uses Deep Learning as the fact algorithmic backbone for all Computer Vision tasks—from image classification and segmentation to scene reconstruction and image synthesis. However, the basis for the success of most algorithms has been the use of an enormous quantity of good-quality labeled data, i.e., examples whose ground truth is already available for training a model (a technique called Supervised Learning).

Data Labeling is a highly laborious process, consuming about 80% of the time dedicated to a Machine Learning project. Even then, some labels may be erroneous, thus adversely affecting the model training. For this reason, current methods focus on reducing the need for labeled training data and utilizing the vast amount of unstructured data available in this information technology era.

Active Learning is one such low supervision method, which belongs to the class of “Semi-Supervised Learning,” a learning paradigm where a small amount of labeled data and a large quantity of unlabeled data are used together to train a model. But actually, what is active learning? Let's look into it.

What is active learning?

Active Learning is a type of machine learning algorithm that actively interacts with its environment to learn from it. It works by selecting the data points that are most useful for training and labeling them, allowing the model to train on fewer labeled data points than conventional supervised learning algorithms. It can also be thought of as an interactive form of semi-supervised learning, as it utilizes both labeled and unlabeled data in order to obtain better results.

Benefits of active learning:

The major benefit of Active Learning is that it reduces the human labor required in a Machine Learning project. By intelligently selecting which data points should be labeled, active learning algorithms can reduce the amount of time spent on labeling large datasets by up to 50%. Additionally, as active learning algorithms are able to detect errors in labeled data, they are also capable of detecting when labels may be incorrect. This ensures that the model is provided with accurate training data and improves its overall performance.


Active Learning can be applied to a wide range of tasks including image classification, natural language processing (NLP), and speech recognition. In addition to reducing the need for manual labeling, active learning can also improve the accuracy of models by selecting only those data points that contain valuable information.

Modern Research into Active Learning:

Researchers have developed several different approaches to active learning, such as query synthesis, uncertainty sampling, and regression-based methods. Techniques like query synthesis use reinforcement learning techniques to select informative data points, while uncertainty sampling methods use information entropy to guide the selection process. Regression-based active learning algorithms take a more general approach and select data points based on their potential contribution to training a model.

When is Active Learning Valuable?

Active Learning can be applied in any situation where labeled data is scarce or expensive to obtain. For example, it can be used to reduce the amount of manual labor required for medical image annotation or text classification tasks. Even when labels are not difficult to obtain, active learning techniques can still be used to improve the accuracy of models by selecting only those data points that contain valuable information. In other words, active learning can help machine learning models make more accurate predictions with fewer labeled examples.

Active Learning Query Strategies

When it comes to active learning, one of the most important components is formulating an appropriate query strategy. This query strategy should aim to minimize human effort while maximizing model performance. Popular strategies for formulating queries include uncertainty sampling, query by committee, and pool-based sampling. Uncertainty sampling focuses on selecting data points with the highest entropy or information gain potential. Query by committee aims to select data points that disagree among multiple model predictions. Pool-based sampling selects data points randomly from a large pool of unlabeled examples.

In conclusion, Active Learning is a powerful semi-supervised learning tool that can reduce the amount of manual labor required in Machine Learning projects and improve model accuracy at the same time. It works by intelligently selecting data points that are most valuable for training a model. Common query strategies include uncertainty sampling, query by committee, and pool-based sampling. Active learning can be applied to any task where labeled data is scarce or expensive to obtain.


Active Learning has widespread practical applications in all domains of Artificial Intelligence due to its ability to produce optimal performance even with few labeled samples. It is commonly used instead of traditional Supervised Learning, saving a lot of resources for ML teams. Let us look into some of them next.

  • Image Classification: As labeled data is expensive to obtain, Active Learning can be used in image classification tasks to reduce the amount of manually annotated data required. It selects the most informative images from a large pool of unlabeled examples, which allows models to learn faster and more accurately.
  • Natural Language Processing (NLP): Active Learning can also be applied to NLP tasks such as text classification and sentiment analysis. In these tasks, active learning algorithms can select data points that contain valuable information while reducing the need for manual labeling.
  • Speech Recognition: Active Learning techniques have been shown to improve speech recognition accuracy by selecting only those audio samples that are likely to contain useful information. This reduces the number of false positives
  • Computer Vision: Active Learning is also used in computer vision applications to reduce the amount of labeled data required for training models. This allows models to be trained faster and more accurately while requiring less human effort.


We at Lexunit already faced this problem that we had not enough labeled data to train a machine-learning model for a document processing problem. We made our research on the topic and found many interesting ones, but the one we would like to highlight is Snorkel.ai. Snorkel is an active learning library for data-driven AI applications. It allows us to quickly create training data set with a help of a weak classifier. In the end, we solved our problem with synthetic data generation which required a manual template creation process, but as always the whole project was very valuable from a learning standpoint.

In summary, Active Learning is a powerful semi-supervised machine learning technique that can improve model accuracy while reducing the amount of manual labor needed for labeling data. It is widely used in many different domains, from image classification to NLP tasks. Modern research into active learning has led to the development of new algorithms that are more accurate and efficient than ever before. With further research, we can expect more improvements in this field over time.

Sources: Murphy, K. (2012). Machine learning: A probabilistic perspective. MIT Press. Stanford Artificial Intelligence Lab course CS221. Available online courses covering topics related to Active Learning.