Boosting Active Learning via Improving Test Performance (2112.05683v2)

Abstract: Central to active learning (AL) is what data should be selected for annotation. Existing works attempt to select highly uncertain or informative data for annotation. Nevertheless, it remains unclear how selected data impacts the test performance of the task model used in AL. In this work, we explore such an impact by theoretically proving that selecting unlabeled data of higher gradient norm leads to a lower upper-bound of test loss, resulting in better test performance. However, due to the lack of label information, directly computing gradient norm for unlabeled data is infeasible. To address this challenge, we propose two schemes, namely expected-gradnorm and entropy-gradnorm. The former computes the gradient norm by constructing an expected empirical loss while the latter constructs an unsupervised loss with entropy. Furthermore, we integrate the two schemes in a universal AL framework. We evaluate our method on classical image classification and semantic segmentation tasks. To demonstrate its competency in domain applications and its robustness to noise, we also validate our method on a cellular imaging analysis task, namely cryo-Electron Tomography subtomogram classification. Results demonstrate that our method achieves superior performance against the state of the art. Our source code is available at https://github.com/xulabs/aitom/blob/master/doc/projects/al_gradnorm.md.

• The paper demonstrates that selecting data with higher gradient norms minimizes an upper bound on test loss.
• It introduces two schemes—expected-gradnorm and entropy-gradnorm—to compute gradient norms for unlabeled data.
• Empirical results on benchmarks like CIFAR and cryo-ET confirm that the approach outperforms existing active learning methods.

Enhancing Active Learning through Gradient Norm Optimization

The presented paper tackles a pivotal challenge in the field of Active Learning (AL) - optimizing the selection of data for annotation to boost the test performance of machine learning models. The authors introduce an innovative approach centered around the gradient norm, providing a theoretical foundation to enhance AL frameworks.

Central to current AL methodologies is the method of selecting data, which is typically categorized into uncertainty-based and diversity-based selection strategies. However, these prevailing methods leave open questions regarding their direct impact on test performance. The authors address this gap by theoretically demonstrating that selecting data with a higher gradient norm can minimize an upper-bound on the test loss, thereby improving test performance.

The challenge in operationalizing this approach lies in calculating the gradient norm for unlabeled data, as it lacks label information. The authors provide two remedial schemes: the expected-gradnorm and the entropy-gradnorm. The expected-gradnorm approach constructs an expected empirical loss using all possible labels, and the entropy-gradnorm employs the entropy of the output to compute an unsupervised loss, facilitating gradient norm computation.

To evaluate the efficacy of these methodologies, the authors embed them in a universal AL framework and test their performance on classical tasks such as image classification and semantic segmentation. The results are impressive, with the proposed method outperforming existing state-of-the-art AL techniques across a variety of benchmark datasets including Cifar10, Cifar100, SVHN, and more. Noteworthy is the method's robust performance on the challenging cryo-Electron Tomography subtomogram classification task, showcasing its generalizability and effectiveness in domain-specific noisy data.

The implications of this work are multifaceted. Practically, it offers a more targeted approach to data selection in resource-intensive tasks, potentially saving substantial annotation costs while still maximizing model performance. Theoretically, it enriches the understanding of data influence on model outcomes in AL contexts. This paper opens avenues for further research into optimizing gradient-based criteria, potentially integrating other model-centric metrics for enhanced data selection strategies in AI.

This paper represents a strong contribution by positing a rational data selection method in AL through gradient norm, supported by theoretical and empirical evidence. Future work may explore extending these methodologies across broader neural network architectures and domain-specific applications, ensuring adaptability and scalability in diverse and evolving AI landscapes.