Adversarial Sampling for Active Learning
This addresses the challenge of efficient sample selection in active learning for multi-class problems, though it is incremental as it builds on existing GAN and active learning techniques.
The paper tackles the problem of active learning by proposing ASAL, a GAN-based method that generates high-entropy samples and selects similar ones from a pool for annotation, resulting in outperforming random sampling with sub-linear runtime complexity.
This paper proposes asal, a new GAN based active learning method that generates high entropy samples. Instead of directly annotating the synthetic samples, ASAL searches similar samples from the pool and includes them for training. Hence, the quality of new samples is high and annotations are reliable. To the best of our knowledge, ASAL is the first GAN based AL method applicable to multi-class problems that outperforms random sample selection. Another benefit of ASAL is its small run-time complexity (sub-linear) compared to traditional uncertainty sampling (linear). We present a comprehensive set of experiments on multiple traditional data sets and show that ASAL outperforms similar methods and clearly exceeds the established baseline (random sampling). In the discussion section we analyze in which situations ASAL performs best and why it is sometimes hard to outperform random sample selection.