Hey, some research we’ve done in the direction of active learning.

Dealing with a big unlabeled dataset may become very expensive very fast. Therefore it makes sense to invest time into labeling optimization techniques. In the article below, we explore one of the optimizations called active learning. Active Learning is a branch of machine learning that seeks to minimize the total amount of data required for labeling by strategically sampling observations that provide new insight into the problem. In particular, algorithms try to select diverse and informative data for annotation (rather than random observations) from a pool of unlabeled data.

Excited to share:

https://towardsdatascience.com/learn-faster-with-smarter-data-labeling-15d0272614c4