Active One-shot Learning
This addresses the problem of efficient labeling in machine learning for tasks like image classification, though it is incremental as it builds on existing one-shot and reinforcement learning methods.
The paper tackles the problem of active one-shot learning by combining reinforcement learning with one-shot learning to allow models to decide which examples to label during classification, achieving higher prediction accuracy or trading accuracy for fewer label requests.
Recent advances in one-shot learning have produced models that can learn from a handful of labeled examples, for passive classification and regression tasks. This paper combines reinforcement learning with one-shot learning, allowing the model to decide, during classification, which examples are worth labeling. We introduce a classification task in which a stream of images are presented and, on each time step, a decision must be made to either predict a label or pay to receive the correct label. We present a recurrent neural network based action-value function, and demonstrate its ability to learn how and when to request labels. Through the choice of reward function, the model can achieve a higher prediction accuracy than a similar model on a purely supervised task, or trade prediction accuracy for fewer label requests.