1. Querying Easily Flip-flopped Samples for Deep Active Learning
- Author
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Cho, Seong Jin, Kim, Gwangsu, Lee, Junghyun, Shin, Jinwoo, and Yoo, Chang D.
- Subjects
Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Statistics - Machine Learning - Abstract
Active learning is a machine learning paradigm that aims to improve the performance of a model by strategically selecting and querying unlabeled data. One effective selection strategy is to base it on the model's predictive uncertainty, which can be interpreted as a measure of how informative a sample is. The sample's distance to the decision boundary is a natural measure of predictive uncertainty, but it is often intractable to compute, especially for complex decision boundaries formed in multiclass classification tasks. To address this issue, this paper proposes the {\it least disagree metric} (LDM), defined as the smallest probability of disagreement of the predicted label, and an estimator for LDM proven to be asymptotically consistent under mild assumptions. The estimator is computationally efficient and can be easily implemented for deep learning models using parameter perturbation. The LDM-based active learning is performed by querying unlabeled data with the smallest LDM. Experimental results show that our LDM-based active learning algorithm obtains state-of-the-art overall performance on all considered datasets and deep architectures., Comment: 34 pages, 17 figures, 5 tables. Accepted to the 12th International Conference on Learning Representations (ICLR 2024) (ver2: fixed some typos and improved some parts of the writing)
- Published
- 2024