What does "Limited Training Data" mean?
Table of Contents
Limited training data refers to a situation where there is not enough labeled information available for training machine learning models. This is a common challenge, especially in fields like remote sensing and infrared object detection. When there are few examples to learn from, models may struggle to perform effectively.
Importance of Labels
Labels are crucial because they help models understand what they are looking at. In many cases, obtaining these labels can be expensive and time-consuming. As a result, models may have to work with only a handful of labeled examples, which can hurt their ability to recognize patterns and make accurate predictions.
Challenges Faced
When there is limited training data, models might not see enough variety to learn different features of the objects or scenes they need to identify. This can lead to poor performance and a lack of consistency across different views or conditions.
Strategies to Overcome Limitations
Researchers are finding new ways to address the issues caused by limited training data. Some methods involve using information from other data types, such as combining insights from images that have plenty of labels with those that don't. This can help models learn better and make more accurate predictions without needing a large amount of labeled training data.
Conclusion
Limited training data is a significant hurdle in the world of machine learning. However, innovative approaches are being developed to improve model performance even when the amount of labeled information is small.