We propose Disentanglement based Active Learning (DAL), a new active learning technique based on self-supervision which leverages the concept of disentanglement. Instead of requesting labels from human oracle, our method automatically labels the majority of the datapoints, thus drastically reducing the human labeling budget in Generative Adversarial Net (GAN) based active learning approaches. The proposed method uses Information Maximizing Generative Adversarial Nets (InfoGAN) to learn disentangled class category representations. Disagreement between active learner predictions and InfoGAN labels decides if the datapoints need to be human-labeled. We also introduce a label correction mechanism that aims to filter out label noise that occurs due to automatic labeling. Results on three benchmark datasets for the image classification task demonstrate that our method achieves better performance compared to existing GAN-based active learning approaches.
翻译：我们建议基于分解的主动学习(DAL),这是一种基于自我监督的新的积极学习技术,它利用了分解概念的概念。我们的方法不要求从人类神器上贴标签,而是自动标出大多数数据点的标签,从而大幅削减基于基因反转网(GAN)的主动学习方法中的人类标签预算。拟议方法使用信息最大化基因反转网(InfoGAN)来学习分解的类别表达方式。积极学习预测和InfoGAN标签之间的分歧决定了数据点是否需要以人类标出。我们还引入了一种标签校正机制,目的是过滤自动标出因自动标签而出现的标签噪音。关于图像分类任务的三个基准数据集的结果表明,与现有的基于GAN的积极学习方法相比,我们的方法取得了更好的业绩。
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