Active and Continuous Exploration with Deep Neural Networks and Expected Model Output Changes

Understand

The demands on visual recognition systems do not end with the complexity offered by current large-scale image datasets, such as ImageNet.

• In consequence, we need curious and continuously learning algorithms that actively acquire knowledge about semantic concepts which are present in available unlabeled data.
• As a step towards this goal, we show how to perform continuous active learning and exploration, where an algorithm actively selects relevant batches of unlabeled examples for annotation.
• These examples could either belong to already known or to yet undiscovered classes.