PF-MA is a new active learning rule that favors likely-positive uncertain samples to speed up discovery of rare categories in imbalanced visual retrieval.
Active learning literature survey
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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UNVERDICTED 4representative citing papers
A no-regret procedure for safe online logistic classification that meets a target error rate with high probability using only O(sqrt(T)) excess tests over an oracle.
UGEL employs deep beta regression to estimate uncertainty in one forward pass, enabling faster convergence in edge learning for remote sensing image regression than active or semi-supervised baselines.
Adaptive Data Dropout uses performance feedback to dynamically modulate training data exposure, reducing effective steps while matching static dropout accuracy on image benchmarks.
citing papers explorer
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Positive-First Most Ambiguous: A Simple Active Learning Criterion for Interactive Retrieval of Rare Categories
PF-MA is a new active learning rule that favors likely-positive uncertain samples to speed up discovery of rare categories in imbalanced visual retrieval.
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The Good, the Bad, and the Sampled: a No-Regret Approach to Safe Online Classification
A no-regret procedure for safe online logistic classification that meets a target error rate with high probability using only O(sqrt(T)) excess tests over an oracle.
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Uncertainty-Guided Edge Learning for Deep Image Regression in Remote Sensing
UGEL employs deep beta regression to estimate uncertainty in one forward pass, enabling faster convergence in edge learning for remote sensing image regression than active or semi-supervised baselines.
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Adaptive Data Dropout: Towards Self-Regulated Learning in Deep Neural Networks
Adaptive Data Dropout uses performance feedback to dynamically modulate training data exposure, reducing effective steps while matching static dropout accuracy on image benchmarks.