Active Generative Adversarial Network for Image Classification
This addresses the cost and difficulty of obtaining labeled data for image classification tasks, but is incremental as it builds on existing GAN and active learning methods.
The paper tackles the problem of expensive data labeling for image classification by proposing a model that generates informative samples without querying a human oracle, using a conditional GAN guided by a reward based on uncertainty, and shows improved classification performance in evaluations.
Sufficient supervised information is crucial for any machine learning models to boost performance. However, labeling data is expensive and sometimes difficult to obtain. Active learning is an approach to acquire annotations for data from a human oracle by selecting informative samples with a high probability to enhance performance. In recent emerging studies, a generative adversarial network (GAN) has been integrated with active learning to generate good candidates to be presented to the oracle. In this paper, we propose a novel model that is able to obtain labels for data in a cheaper manner without the need to query an oracle. In the model, a novel reward for each sample is devised to measure the degree of uncertainty, which is obtained from a classifier trained with existing labeled data. This reward is used to guide a conditional GAN to generate informative samples with a higher probability for a certain label. With extensive evaluations, we have confirmed the effectiveness of the model, showing that the generated samples are capable of improving the classification performance in popular image classification tasks.