In this article, the authors propose a novel approach to active learning for deep learning models called "query-based active labeling" (QBAL). The main idea is to select informative samples from a large dataset and label them manually, rather than randomly selecting samples and labeling them. The authors propose a query strategy that uses a clustering algorithm to group similar samples together, and then selects the most informative samples within each cluster based on their similarity to known categories. The proposed method is trained in an end-to-end manner by minimizing a total objective function that combines the cross-entropy loss (Lce) and the bisection error (Lbce) with the label uncertainty (Lem) and a budget term (λLt). The authors evaluate their method on several benchmark datasets and show that it outperforms existing active learning methods.
Analysis
The article is focused on developing an efficient and effective active learning strategy for deep learning models. The authors propose a query-based approach that selects informative samples from the dataset, rather than randomly selecting them. The proposed method uses a clustering algorithm to group similar samples together and then selects the most informative samples within each cluster based on their similarity to known categories.
The key insight of the article is that the query strategy can significantly reduce the redundancy of the selected samples, which leads to better performance in active learning. The authors also demonstrate that their method can adapt to different scenarios by adjusting the query strategy and the budget term.
Conclusion
In summary, the article proposes a novel approach to active learning for deep learning models called "query-based active labeling" (QBAL). The proposed method selects informative samples from a large dataset and labels them manually, rather than randomly selecting samples and labeling them. The authors demonstrate that their method outperforms existing active learning methods on several benchmark datasets. The proposed method has important implications for reducing the cost and improving the efficiency of active learning in deep learning applications.
