Active Learning for Reducing Labeling Costs
Last Updated : 23 Jun, 2025
Active Learning (AL) has emerged as an important strategy to optimize the labeling process, whether it’s annotating medical images, moderating social media content labeling large datasets often requires domain experts, time and money. Active Learning aims to achieve high model performance using fewer labeled samples.
Cost of Labeling
Supervised learning models rely on large quantities of labeled data and the labeling process can be:
- Time-intensive: Especially for images, video or long-form text.
- Costly: Requires human annotators, sometimes domain experts like doctors or lawyers.
- Imbalanced: In many datasets, informative or rare examples are few and far between.
- Scalability-constrained: Human annotation simply doesn't scale as fast as data generation.
Active Learning
Active learning is a subset of machine learning in which the model selectively queries the most informative data points from an unlabeled pool to be labeled by usually a human annotator. Instead of labeling all data blindly, the system identifies which examples will most improve the model if labeled.
Active Learning CycleThe standard active learning loop involves:
- Start with a small labeled dataset and a large unlabeled pool.
- Train an initial model on the labeled data.
- Use a strategy to select informative examples from the unlabeled pool.
- Label the selected examples via a human annotator.
- Add them to the training set and repeat.
This iterative process continues until performance plateaus or the labeling budget is exhausted.
Why Active Learning Works
The effectiveness of active learning lies in its ability to identify high value data points that:
- Are hard to classify with current knowledge.
- Represent underrepresented areas of the feature space.
- Help refine decision boundaries in the model.
Instead of treating all samples equally, active learning applies an approach focusing human effort to where it has the greatest payoff.
Core Strategies in Active Learning
Several strategies have been developed to decide which data points are worth labeling. Here are the most common ones:
1. Uncertainty Sampling: Uncertainty Sampling is the most widely used technique. The model queries the examples it is least confident about. For a classification problem, this may be where the top predicted class has low probability or where the difference between top predictions is small.
Example : In a binary classifier, a data point with a predicted probability of 0.51 for class A and 0.49 for class B is more uncertain, than one with 0.99 and 0.01.
2. Query by Committee (QBC): In Query by Committee(QBC) multiple models are trained on the same labeled dataset. For each unlabeled sample, disagreement among the committee members is measured. Samples with the highest disagreement are chosen. This exploits model diversity to identify conflicting regions of the input space.
3. Diversity Sampling: Diversity sampling ensures wide coverage of the data distribution by selecting examples that are different from each other. It prevents the model from overfitting on narrow regions. Clustering and core-set techniques are often used here.
4. Expected Model Change: This technique selects examples that, if labeled, would lead to the greatest change in model parameters. Though computationally intensive, it directly targets model improvement.
5. Hybrid Approaches: Real-world systems often combine multiple strategies like using uncertainty sampling first, then filtering for diversity to balance exploitation and exploration.
Practical Workflow
Let’s walk through a simplified example to illustrate the power of active learning. Suppose you're building a spam detection system. You have:
- 1,000 labeled emails.
- 10,000 unlabeled emails.
- A budget to label 1,000 more emails.
Without Active Learning: You randomly sample 1,000 emails to label. Many may be redundant or uninformative (easy-to-classify spam).
With Active Learning: You use uncertainty sampling to select emails the model struggles with, like ambiguous ones. These are more likely to improve the decision boundary between spam and non-spam. Studies show that active learning can often match or exceed the performance of fully supervised learning while labeling only 30–50% of the data.
Key Benefits of Active Learning
- Reduced Labeling Costs: Fewer examples need to be labeled for comparable model performance.
- Faster Time-to-Model: With fewer labels needed, models can be deployed sooner.
- Improved Model Generalization: Strategic sample selection can expose blind spots in the data.
- Scalable Human-in-the-Loop: Human feedback is used efficiently and effectively.
Some popular tools that support active learning include:
- modAL: A Python framework built on scikit-learn.
- libact: A more academic-focused library with support for various querying strategies.
- Label Studio + Active Learning: Easily integrates with labeling workflows.
- Snorkel: It is focused on weak supervision, it complements AL in reducing labeling efforts.
Applications Across Domains
Active learning is domain-agnostic and has found success in many real-world applications:
- Healthcare: Labeling medical scans like MRIs or pathology slides is expensive and requires radiologists. Active learning can select edge cases and uncertain diagnoses for labeling, improving diagnostic tools faster.
- Search Engines: In relevance feedback systems, AL is used to optimize click data labeling and user intent modeling.
- Document Classification: Legal documents or contracts often need to be categorized. Active learning ensures only the most borderline cases are sent to human reviewers.
- Computer Vision: In object detection tasks, AL selects images with overlapping or unclear bounding boxes for expert annotation, increasing model precision with fewer annotations.
- NLP Tasks: In tasks like sentiment analysis and entity recognition, AL identifies ambiguous sentences and slangs that need human clarification.
Challenges for Active Learning
- Cold Start Problem: The initial model that is trained on a small labeled set, may be weak. This can lead to poor selection of the first few samples. Careful initialization is important.
- Noisy Oracles: If human annotators are inaccurate, active learning can amplify labeling errors since it focuses effort on the most uncertain data.
- Strategy Selection: There is default querying strategy. It often requires experimentation to find what works best for a specific problem.
- Batch Selection: In practice, samples are labeled in batches rather than one at a time. Selecting diverse yet informative batches is harder than picking individual examples.