Adversarial Sampling for Active Learning

This paper describes ASAL a new active learning strategy that uses uncertainty sampling, adversarial sample generation and sample matching. Compared to traditional pool-based uncertainty sampling strategies, ASAL synthesizes uncertain samples instead of performing an exhaustive search in each active learning cycle. Then, the sample matching efficiently selects similar samples from the pool. We present a comprehensive set of experiments on MNIST and CIFAR-10 and show that ASAL outperforms similar methods and clearly exceeds passive learning. To the best of our knowledge this is the first pool-based adversarial active learning technique and the first that is applied for multi-label classification using deep convolutional classifiers.