Deep Active Learning by Leveraging Training Dynamics
This work addresses the problem of unreliable empirical criteria in deep active learning for researchers and practitioners, offering a theory-driven approach that is incremental but with practical improvements.
The authors tackled the lack of theoretical justification in deep active learning by proposing dynamicAL, a method that selects samples to maximize training dynamics, proving a correlation with generalization under ultra-wide conditions and achieving consistent outperformance over baselines with scalable implementation.
Active learning theories and methods have been extensively studied in classical statistical learning settings. However, deep active learning, i.e., active learning with deep learning models, is usually based on empirical criteria without solid theoretical justification, thus suffering from heavy doubts when some of those fail to provide benefits in real applications. In this paper, by exploring the connection between the generalization performance and the training dynamics, we propose a theory-driven deep active learning method (dynamicAL) which selects samples to maximize training dynamics. In particular, we prove that the convergence speed of training and the generalization performance are positively correlated under the ultra-wide condition and show that maximizing the training dynamics leads to better generalization performance. Furthermore, to scale up to large deep neural networks and data sets, we introduce two relaxations for the subset selection problem and reduce the time complexity from polynomial to constant. Empirical results show that dynamicAL not only outperforms the other baselines consistently but also scales well on large deep learning models. We hope our work would inspire more attempts on bridging the theoretical findings of deep networks and practical impacts of deep active learning in real applications.