Active learning with randomly initialized models achieves comparable results to traditional candidate-model methods, with low-confidence sampling proving most effective.
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Develops a multi-task learning based adversarial training approach to improve robustness of object detectors to adversarial attacks, with experiments on PASCAL-VOC and MS-COCO.
Proposes LCD and three other hybrid uncertainty-diversity sampling methods for active learning that outperform prior approaches by selecting uncertain yet diverse samples.
citing papers explorer
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Are Candidate Models Really Needed for Active Learning?
Active learning with randomly initialized models achieves comparable results to traditional candidate-model methods, with low-confidence sampling proving most effective.
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Towards Adversarially Robust Object Detection
Develops a multi-task learning based adversarial training approach to improve robustness of object detectors to adversarial attacks, with experiments on PASCAL-VOC and MS-COCO.
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Balancing Uncertainty and Diversity of Samples: Leveraging Diversity of Least, High Confidence Samples for Effective Active Learning
Proposes LCD and three other hybrid uncertainty-diversity sampling methods for active learning that outperform prior approaches by selecting uncertain yet diverse samples.