Interpreting pretext tasks for active learning: a reinforcement learning approach

Interpreting pretext tasks for active learning: a reinforcement learning approach

Blog Article

Abstract As the amount of labeled data increases, the performance of deep neural networks tends to improve.However, annotating a large volume of data can be expensive.Active learning addresses this challenge by selectively annotating unlabeled data.There have been recent attempts to incorporate self-supervised learning Stopper into active learning, but there are issues in utilizing the results of self-supervised learning, i.

e., it is uncertain how these should be interpreted in the context of active learning.To address this issue, we propose a multi-armed Display Boards bandit approach to handle the information provided by self-supervised learning in active learning.Furthermore, we devise a data sampling process so that reinforcement learning can be effectively performed.

We evaluate the proposed method on various image classification benchmarks, including CIFAR-10, CIFAR-100, Caltech-101, SVHN, and ImageNet, where the proposed method significantly improves previous approaches.