AI models are excellent at tasks such as image classification, object detection, and sentiment analysis. However, building a strong supervised learning model requires a large amount of labeled data, which can be time-consuming and prone to errors.
To handle the issue of limited labeled data, there are several approaches:
- Weak Supervision
- Self-Supervised Learning
- Transfer Learning
- Active Learning
Weak Supervision
Weak supervision enables the labeling of unlabeled data using label functions (LFs). LFs use pre-defined heuristics to automatically categorize the data, similar to how humans use shortcuts to identify objects, such as recognizing a cat or dog based on their ears.
The LFs can be adjusted for different use cases and domains, such as a label function for text toxicity classification that searches for specific keywords like “HATE,” “KILL,” and “DESPISE” to label the text as toxic.
However, this automated labeling process can result in imprecise data, so it’s best to pair it with human oversight for quality assurance. The LFs perform bulk labeling, while a human verifies the accuracy of the labeled data.
Self-Supervised Learning
Self-supervised learning is a technique that trains a model using a small labeled dataset. The trained model can then be used to label more data, which can then be used to train a stronger model. This process of training → labeling → training → labeling → …. can be repeated until you have a model that meets your desired performance.
This method is powerful because it allows you to build a strong model with a limited amount of labeled data by iteratively adding more labeled data as the model improves.
Transfer Learning
Transfer learning is a widely used technique due to its effectiveness. It involves using a pre-trained model that has been trained on a large labeled dataset. This model’s knowledge can then be applied to your specific task.
The power of transfer learning lies in the fact that the pre-trained model has already learned key information through its training process. For example, a pre-trained Convolutional Neural Network (CNN) on ImageNet has the ability to detect edges, curves, and shapes, which can then be applied to other tasks like classifying between a car and a truck.
Active Learning
Active learning is a method that makes labeling data more efficient. Instead of labeling all data, the model chooses which samples to label based on their potential to improve the training process.
For instance, an image classification model (called the active learner) using active learning will prioritize labeling images where the model is not confident in its predictions. This way, the model can better learn the difference between true and false predictions.
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