Mitigating Sampling Bias and Improving Robustness in Active Learning

no code implementations • 13 Sep 2021 • Ranganath Krishnan, Alok Sinha, Nilesh Ahuja, Mahesh Subedar, Omesh Tickoo, Ravi Iyer

This paper presents simple and efficient methods to mitigate sampling bias in active learning while achieving state-of-the-art accuracy and model robustness.

Active Learning

Robust Contrastive Active Learning with Feature-guided Query Strategies

no code implementations • 13 Sep 2021 • Ranganath Krishnan, Nilesh Ahuja, Alok Sinha, Mahesh Subedar, Omesh Tickoo, Ravi Iyer

We introduce supervised contrastive active learning (SCAL) and propose efficient query strategies in active learning based on the feature similarity (featuresim) and principal component analysis based feature-reconstruction error (fre) to select informative data samples with diverse feature representations.

Active Learning Image Classification +1