Neural Active Learning with Efficient Exploitation and Exploration Networks
We propose two novel algorithms, NEURONAL-S and NEURONAL-P, for stream-based and pool-based active learning that mitigate the adverse impacts of the number of classes (K) on the performance and computational costs of existing bandit-based approaches. Our methods achieve improved theoretical guarantees and empirical performance.