What does "Pseudo Label Generation" mean?
Table of Contents
Pseudo label generation is a technique used in machine learning to help models learn from data that is not fully labeled. Instead of needing complete labels for every piece of data, the model can create its own labels based on patterns it recognizes. This is especially useful when some classes of data have many examples, while others have very few.
How It Works
- Initial Training: The model starts by using available labeled data to learn basic patterns.
- Creating Pseudo Labels: Once the model has some understanding, it looks at unlabeled data and tries to guess what the labels might be. These guesses are called pseudo labels.
- Refining the Model: The model then uses these pseudo labels to improve its understanding and learn more about the data.
- Feedback Loop: This process can be repeated, allowing the model to continually improve as it learns from its own guesses.
Benefits
- Cost-Effective: It reduces the need for extensive manual labeling, saving time and money.
- Better Learning: It helps the model learn even from imbalanced datasets, where some categories have fewer examples.
- Higher Accuracy: By refining its understanding through guessing, the model can achieve better results over time.
Overall, pseudo label generation is a valuable approach that helps machines learn more effectively, especially when working with limited labeled data.