A Beginner’s Guide to Weak Supervision

What is Weak Supervision?

Weak supervision is a paradigm in machine learning that leverages imperfect, incomplete, and noisy labels to train models. Unlike traditional supervised learning that requires a large amount of accurately labeled data, weak supervision allows the use of various strategies to generate labels that might not be entirely correct. This approach can significantly reduce the time and cost associated with data annotation, making it a powerful tool for developing models in a more efficient manner.

Why Use Weak Supervision?

There are several reasons why one might opt to use weak supervision:

  • Scalability: Generating hand-labeled data can be time-consuming and expensive. Weak supervision allows the use of scalable data labeling strategies.
  • Data Variety: This approach can integrate various sources of labeling information, offering a richer dataset for model training.
  • Faster Iteration: With the ability to quickly generate labels, weak supervision accelerates the model development and iteration process.

Types of Weak Supervision

Distant Supervision

Distant supervision involves using external resources like databases, ontologies, or knowledge bases to generate training labels. This method assumes that the information contained in these resources is reasonably accurate for the task at hand. For example, a biomedical text classification model might use a database of medical terms to generate labels.

Heuristic Labeling

Heuristic labeling involves using heuristic rules, patterns, and simpler models to generate training labels. These heuristics can be written as regular expressions or conditions that approximate the labeling process. Although these labels may be noisy, they can still provide valuable information to the model.

Data Augmentation

Data augmentation involves creating new labeled examples by transforming existing ones. Common transformations include rotations, translations, and flipping images in computer vision tasks. While this approach is more likely to be used for image data, text data can also benefit from transformations like synonym replacement or back-translation.

Combining Weak Label Sources

One of the crucial aspects of weak supervision is combining multiple weak label sources to create a more robust and comprehensive training dataset. Tools like Snorkel focus on this aspect by using a generative model to estimate the accuracy of each labeling source and then combine them in a weighted manner. This process helps in mitigating the noise and improving the overall label quality.

Challenges and Limitations

While weak supervision offers many advantages, several challenges need to be addressed:

  • Label Noise: The generated labels can be noisy and less accurate, potentially causing the model to learn incorrect patterns.
  • Bias: Heuristic methods may introduce biases that can be hard to detect and counteract.
  • Complexity: Combining multiple weak labels effectively often requires sophisticated techniques and domain expertise.

Getting Started

If you are new to weak supervision, here are some initial steps to get you started:

  1. Identify the task and data: Understand the problem you’re solving and gather the relevant data.
  2. Select and implement labeling strategies: Choose suitable weak supervision methods based on your task and data.
  3. Combine labels: Use tools and techniques, such as Snorkel, to consolidate multiple weak labels into a single high-quality label set.
  4. Train and evaluate: Train your machine learning model with the generated labels and evaluate its performance against a validation set.


Weak supervision offers a practical and efficient approach to training machine learning models, especially when high-quality labeled data is scarce. By leveraging various weak labeling strategies and effectively combining them, one can build powerful models with less effort and cost. While there are challenges and limitations, ongoing research and tool development continue to enhance the capabilities and applicability of weak supervision in real-world scenarios.