Active Learning on MNIST – Saving on Labeling

Active Learning is a semi-supervised technique that allows labeling less data by selecting the most important samples from the learning process (loss) standpoint. It can have a huge impact on the project cost in the case when the amount of data is large and the labeling rate is high. For example, object detection and NLP-NER problems.