Active Learning for Reducing Labeling Costs in Machine Learning

In this paper, we explore the use of active learning (AL) for image classification tasks where data is scarce or expensive to obtain. AL involves actively selecting the most informative images to be labeled by a human oracle, rather than randomly sampling the entire dataset. The goal is to reduce the number of labels needed to achieve a certain level of accuracy, thus saving time and resources.
To evaluate the performance of AL algorithms, we introduce the concept of data efficiency (DE). DE measures how many random samples would be needed to match the validation accuracy achieved by an AL method. We found that some AL methods are more efficient than others, and the best approach depends on the specific task and dataset.
We tested different AL strategies on a multi-domain image classification task, where the dataset contains images from various sources with varying levels of complexity. Our results show that the best combination of AL and classifier architecture leads to better performance than using a single algorithm or random sampling.
Our findings have important implications for real-world applications where labeled data is limited, such as medical image analysis, autonomous driving, and facial recognition. By using active learning to efficiently label the most informative images, we can improve the accuracy of these systems without requiring a large amount of labeled data.
In summary, this paper provides a comprehensive evaluation of active learning for multi-domain image classification tasks, demonstrating its potential to improve the efficiency of labeling and reduce the overall cost of training. By introducing data efficiency as a metric and identifying the best AL strategies for different task and dataset combinations, we pave the way for more accurate and efficient machine learning models in various applications.