the lack of labeled training data. This research has resulted in the Snorkel research project and 60+ peer-reviewed publications. Snorkel’s technology which applies weak supervision has been developed and deployed with Google, Intel, DARPA, Stanford Medicine, and more.
What is Weak Supervision and How Does Weak Supervision Work?
Weak supervision is an approach to machine learning in which high-level and often noisier sources of supervision are used to create much larger training sets much more quickly than could otherwise be produced by manual supervision (i.e. labeling examples manually, one by one).
If you have high-level, scalable, but potentially noisy sources of signal, you can combine them using multiple sources of supervision. At Snorkel AI, we use labeling functions to do this.
When Should Weak Supervision Be Used?
Weak supervision enables the creation of very large training sets very quickly. If your particular problem would be better addressed with 100,000 “pretty good” labels, compared to 100 “perfect” labels, it may be worth looking at higher-level interfaces for gathering more data.
Additionally, weak supervision is great to use in any situation in which you need to adapt and iterate regularly and rapidly. If there are frequent shifts in the distribution of your data, such as in an adversarial setting (such as fraud detection) or just because your needs frequently change, weak supervision enables you to do anything from adding novel classes to incorporating and reflecting new realities about your problem.
Weak Supervision vs. Rule-Based Classifiers
Weak supervision has some similarities—and some very important differences—to rule-based classifiers. The obvious similarity is that the inputs to each look like rules (i.e., simple functions that output labels or predictions). The important difference between them is that the rule-based classifier stops there—the rules are the classifier. Such systems are generally brittle because they do not generalize to other examples, even ones that are very similar to those that are labeled by one or more rules.
With weak supervision, on the other hand, the rules (or “labeling functions”) are used to create a training set for a machine-learning-based model. That model can be much more powerful, utilize a much richer feature set, and take advantage of other state-of-the-art techniques in machine learning, such as transfer learning from foundation models. As a result, the model is generally much more robust than a corresponding rule-based classifier.
How Snorkel Flow Makes Weak Supervision Practical
Snorkel AI has applied weak supervision to many problems over the years and we have learned a lot about which features and workflows make it most accessible and practical for users. We built Snorkel Flow specifically with that experience in mind.
Snorkel Flow is a data-centric platform for building AI applications powered by weak supervision and other modern machine learning techniques. In Snorkel Flow, users manage data throughout the full AI lifecycle by writing simple programs (labeling function) to label, manipulate, and monitor training data. These programmatic inputs are modeled and integrated using theoretically-grounded statistical techniques, made accessible to both developer and non-developer users alike via both a no-code UI and Python SDK.
Snorkel Flow provides you with the ability to easily express many different types of signal, whether that is importing existing labels or models that you already have and applying them, or allowing you to write new labeling functions that are rule- or heuristic-based. Snorkel Flow then gives you access to the label-model algorithms that we have developed. They automatically combine these different scalable (but potentially noisy) sources of supervision to create high-quality labels for each of your data points.
Lastly, Snorkel Flow equips you with ready-made infrastructure for the application of these functions. The platform guides you through the process and supplies integrated model training so that you can loop back and make adjustments yourself to your weak supervision sources as you go.
Use Cases for Weak Supervision
Weak supervision can be applied to many problems. The Snorkel AI team has applied it to text data (long and short), conversations, time series, PDFs, images, videos, and more. So long as domain-relevant resources exist or labeling heuristics can be described, weak supervision can be applied. Some of the use cases include:
Some of the use cases include:
- Text and document classification
- Information extraction from unstructured text, PDF, HTML and more
- Rich document processing
- Structured data classification
- Conversational AI and utterance classification
- Entity linking
- Image and cross-modal classification
- Time series analysis
- Video classification
How is it Different from Other Approaches to Machine Learning?
Where to Learn More about Weak Supervision
Accelerate AI with Weak Supervision
It’s clear that weak supervision can be instrumental to not only automating the labeling process while keeping human-in-the-loop but also to accelerating AI development.
But where do you start? Rather than dealing with annotation guides and contracts for crowdsourced labeling, see Snorkel Flow in action.
With Snorkel Flow, Fortune 500 organizations such as Chubb, BNY Mellon, and several government agencies have built accurate and adaptable AI applications fast by putting weak supervision to use.