论文标题
积极学习和逐渐学习，有弱的监督
Active and Incremental Learning with Weak Supervision
论文作者
论文摘要
大量标记的培训数据是深层模型过去取得巨大成功的主要因素之一。由于资金和专业知识的要求，对基准以外的任务的标签获取可能构成挑战。通过选择在模型改进方面有希望的未标记示例，并且仅要求提供各自的标签，积极的学习可以提高标签过程的效率，以时间和成本在时间和成本方面。 在这项工作中，我们描述了一个增量学习方案和主动学习方法的组合。这些允许连续探索新观察到的未标记数据。我们描述了基于模型不确定性以及预期模型输出变化（EMOC）的选择标准。对象检测任务在Pascal VOC数据集的连续探索上下文中进行评估。我们还根据现实世界中的生物多样性应用程序中的主动学习和增量学习验证了一个弱监督的系统，其中分析了相机陷阱的图像。通过接受或拒绝通过我们的方法产生的提案仅标记32张图像的准确性从25.4％提高到42.6％。
Large amounts of labeled training data are one of the main contributors to the great success that deep models have achieved in the past. Label acquisition for tasks other than benchmarks can pose a challenge due to requirements of both funding and expertise. By selecting unlabeled examples that are promising in terms of model improvement and only asking for respective labels, active learning can increase the efficiency of the labeling process in terms of time and cost. In this work, we describe combinations of an incremental learning scheme and methods of active learning. These allow for continuous exploration of newly observed unlabeled data. We describe selection criteria based on model uncertainty as well as expected model output change (EMOC). An object detection task is evaluated in a continuous exploration context on the PASCAL VOC dataset. We also validate a weakly supervised system based on active and incremental learning in a real-world biodiversity application where images from camera traps are analyzed. Labeling only 32 images by accepting or rejecting proposals generated by our method yields an increase in accuracy from 25.4% to 42.6%.
