Reliable Active Learning via Influence Functions

Meng Xia, Ricardo Henao

Abstract: Due to the high cost and time-consuming nature of collecting labeled data, having insufficient labeled data is a common challenge that can negatively impact the performance of deep learning models when applied to real-world applications. Active learning (AL) aims to reduce the cost and time required for obtaining labeled data by selecting valuable samples during model training. However, recent works have pointed out the performance unreliability of existing AL algorithms for deep learning (DL) architectures under different scenarios, which manifests as their performance being comparable (or worse) to that of basic random selection. This behavior compromises the applicability of these approaches. We address this problem by proposing a theoretically motivated AL framework for DL architectures. We demonstrate that the most valuable samples for the model are those that, unsurprisingly, improve its performance on the entire dataset, most of which is unlabeled, and present a framework to efficiently estimate such performance (or loss) via influence functions, pseudo labels and diversity selection. Experimental results show that the proposed reliable active learning via influence functions (RALIF) can consistently outperform the random selection baseline as well as other existing and state-of-the art active learning approaches.

Submission Length: Long submission (more than 12 pages of main content)

Changes Since Last Submission: Thank you for all the valuable suggestions from the reviewers and action editor. We have carefully revised our paper based on their comments. The camera ready version of our paper can be found in the attached PDF. Detailed responses to each reviewer's and action editor's comments can be found in the review section of each reviewers and comment section of action editor.

Assigned Action Editor: ~Charles_Xu1

License: Creative Commons Attribution 4.0 International (CC BY 4.0)

Submission Number: 1364