International Society for Optics and Photonics
abstract Active learning methods have gained popularity to reduce human effort in annotating examples in order to train a classifier. When faced with large amounts of data, the active learning algorithm automatically selects appropriate data samples that are most relevant to train the classifier. Typical active learning approaches select one data instance (one face image, for example) in one iteration of the algorithm, and the classifier is trained with the selected data instances, one-by-one. Instead, there have been very recent efforts ...