Active Learning at the ImageNet Scale

Understand

Active learning (AL) algorithms aim to identify an optimal subset of data for annotation, such that deep neural networks (DNN) can achieve better performance when trained on this labeled subset.

• AL is especially impactful in industrial scale settings where data labeling costs are high and practitioners use every tool at their disposal to improve model performance.
• The recent success of self-supervised pretraining (SSP) highlights the importance of harnessing abundant unlabeled data to boost model performance.
• By combining AL with SSP, we can make use of unlabeled data while simultaneously labeling and training on particularly informative samples.