论文标题
深贝叶斯活跃学习，简要介绍了最近的进步
Deep Bayesian Active Learning, A Brief Survey on Recent Advances
论文作者
论文摘要
主动学习框架提供有效的数据注释，而无需明显的准确性降解。换句话说，主动学习开始使用少量标记的数据训练模型，同时探索未标记数据的空间，以选择要标记的最有用的样本。一般而言，代表不确定性在任何活跃的学习框架中都是至关重要的，但是，深度学习方法无法代表或操纵模型不确定性。另一方面，从现实世界应用的角度来看，不确定性表示在机器学习社区中越来越关注。深贝叶斯活跃的学习框架以及通常任何贝叶斯活跃的学习设置，在模型中提供了实际的考虑，允许使用小型数据进行培训，同时代表模型的不确定性，以进行进一步的有效培训。在本文中，我们简要调查了贝叶斯活跃学习，尤其是深贝叶斯活跃学习框架的最新进展。
Active learning frameworks offer efficient data annotation without remarkable accuracy degradation. In other words, active learning starts training the model with a small size of labeled data while exploring the space of unlabeled data in order to select most informative samples to be labeled. Generally speaking, representing the uncertainty is crucial in any active learning framework, however, deep learning methods are not capable of either representing or manipulating model uncertainty. On the other hand, from the real world application perspective, uncertainty representation is getting more and more attention in the machine learning community. Deep Bayesian active learning frameworks and generally any Bayesian active learning settings, provide practical consideration in the model which allows training with small data while representing the model uncertainty for further efficient training. In this paper, we briefly survey recent advances in Bayesian active learning and in particular deep Bayesian active learning frameworks.
