REVIEW 4 cited by
A Comparative Survey of Deep Active Learning
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
While deep learning (DL) is data-hungry and usually relies on extensive labeled data to deliver good performance, Active Learning (AL) reduces labeling costs by selecting a small proportion of samples from unlabeled data for labeling and training. Therefore, Deep Active Learning (DAL) has risen as a feasible solution for maximizing model performance under a limited labeling cost/budget in recent years. Although abundant methods of DAL have been developed and various literature reviews conducted, the performance evaluation of DAL methods under fair comparison settings is not yet available. Our work intends to fill this gap. In this work, We construct a DAL toolkit, DeepAL+, by re-implementing 19 highly-cited DAL methods. We survey and categorize DAL-related works and construct comparative experiments across frequently used datasets and DAL algorithms. Additionally, we explore some factors (e.g., batch size, number of epochs in the training process) that influence the efficacy of DAL, which provides better references for researchers to design their DAL experiments or carry out DAL-related applications.
Forward citations
Cited by 4 Pith papers
-
How Many Labels Are Enough? ALDA: Active Learning Deployment Advisor for Medical Image Classification
ALDA fits learning curves from a short pilot to recommend the active learning strategy that reaches a clinical accuracy target with fewest labels and least sensitivity to target changes.
-
ALScope: A Unified Toolkit for Deep Active Learning
ALScope provides a unified deep active learning benchmark across CV and NLP under standard, open-set, and imbalanced settings, and finds that no algorithm consistently dominates and non-standard settings remain unsolved.
-
TAPS : Frustratingly Simple Test Time Active Learning for VLMs
TAPS queries uncertain test images in a single-sample stream, stores them in a class-balanced buffer, and updates CLIP prompts to achieve small improvements over test-time tuning baselines.
-
Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification
DECERN selects annotation samples by combining a fusion-based uncertainty score with a diversity calibration that balances closeness to uncertainty-weighted cluster centers and distance from known class anchors.
Discussion (0). Sign in to comment.