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Related Experiment Videos

Active learning for noisy oracle via density power divergence.

Yasuhiro Sogawa1, Tsuyoshi Ueno, Yoshinobu Kawahara

  • 1The Institute of Scientific and Industrial Research, Osaka University, 8-1, Mihogaoaka, Ibaraki, Osaka, Japan. sogawa@ar.sanken.osaka-u.ac.jp

Neural Networks : the Official Journal of the International Neural Network Society
|June 4, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a robust active learning framework using density power divergence to accurately estimate models despite noisy labels. The novel approach improves query selection and performance on benchmark and real-world datasets.

Keywords:
Active learningDensity power divergenceNoisy oracle

Related Experiment Videos

Area of Science:

  • Machine Learning
  • Statistical Learning Theory

Background:

  • Active learning accuracy is compromised by noisy labels from oracles.
  • Robustness to label noise is crucial for effective machine learning.

Purpose of the Study:

  • To develop a novel pool-based active learning framework resilient to noisy labels.
  • To introduce robust measures for query selection in active learning.

Main Methods:

  • Utilizing density power divergence (e.g., β-divergence, γ-divergence) for robust model estimation.
  • Developing query selection measures based on these divergences.
  • Proposing an evaluation scheme using asymptotic statistical analyses for direct estimation error evaluation.

Main Results:

  • The proposed framework accurately estimates models even with noisy labels.
  • The developed query selection measures enhance active learning performance.
  • Experimental results demonstrate superior performance over baseline methods on benchmark and real-world image datasets.

Conclusions:

  • Density power divergence offers a robust approach to mitigate noisy label impact in active learning.
  • The novel active learning scheme provides improved accuracy and performance.
  • The proposed evaluation scheme enables direct assessment of estimation error.