Applying Active Learning to Computer Vision
In this chapter, we will dive into using active learning techniques for computer vision tasks. Computer vision involves analyzing visual data such as images and videos to extract useful information. It relies heavily on machine learning models such as convolutional neural networks. However, these models require large labeled training sets, which can be expensive and time-consuming to obtain. Active ML provides a solution by interactively querying the user to label only the most informative examples. This chapter demonstrates how to implement uncertainty sampling for diverse computer vision tasks. By the end, you will have the tools to efficiently train computer vision models with optimized labeling effort. The active ML methods presented open up new possibilities for building robust vision systems with fewer data requirements.
By the end of this chapter, you will be able to do the following:
- Implementing active ML for an image...
