Picking groups instead of samples: A close look at Static Pool-based Meta-Active Learning
This work addresses the challenge of efficient label acquisition in machine learning, but it appears incremental as it extends existing approaches without claiming major breakthroughs.
The paper tackles the problem of selecting samples for annotation in static pool-based meta-active learning by proposing a method that considers the entire selected subset when choosing each sample, rather than selecting samples independently.
Active Learning techniques are used to tackle learning problems where obtaining training labels is costly. In this work we use Meta-Active Learning to learn to select a subset of samples from a pool of unsupervised input for further annotation. This scenario is called Static Pool-based Meta- Active Learning. We propose to extend existing approaches by performing the selection in a manner that, unlike previous works, can handle the selection of each sample based on the whole selected subset.