SDRS uses designed experiments and ANOVA decomposition on synthetic data to identify Type I coverage gaps and Type II spurious dependencies in vision models, then generates targeted data to improve performance.
Generative Adversarial Active Learning
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
abstract
We propose a new active learning by query synthesis approach using Generative Adversarial Networks (GAN). Different from regular active learning, the resulting algorithm adaptively synthesizes training instances for querying to increase learning speed. We generate queries according to the uncertainty principle, but our idea can work with other active learning principles. We report results from various numerical experiments to demonstrate the effectiveness the proposed approach. In some settings, the proposed algorithm outperforms traditional pool-based approaches. To the best our knowledge, this is the first active learning work using GAN.
verdicts
UNVERDICTED 5representative citing papers
DAL poses batch active learning as a binary classification task between labeled and unlabeled data to select informative examples for labeling.
Real crowd-sourced text annotations are used to test eight active learning techniques, showing their behavior under actual noisy labeling and refusals unlike simulated oracles.
PAL is a portable active learning method for object detection that uses class-specific logistic classifiers for uncertainty and image-level diversity to select annotation batches, showing better label efficiency than baselines on COCO, VOC, and BDD100K.
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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Synthetic Designed Experiments for Diagnosing Vision Model Failure
SDRS uses designed experiments and ANOVA decomposition on synthetic data to identify Type I coverage gaps and Type II spurious dependencies in vision models, then generates targeted data to improve performance.
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Discriminative Active Learning
DAL poses batch active learning as a binary classification task between labeled and unlabeled data to select informative examples for labeling.
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An Analysis of Active Learning Algorithms using Real-World Crowd-sourced Text Annotations
Real crowd-sourced text annotations are used to test eight active learning techniques, showing their behavior under actual noisy labeling and refusals unlike simulated oracles.
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Portable Active Learning for Object Detection
PAL is a portable active learning method for object detection that uses class-specific logistic classifiers for uncertainty and image-level diversity to select annotation batches, showing better label efficiency than baselines on COCO, VOC, and BDD100K.
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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.