使用深度和主动学习降低构建数据集的成本

Filipa Castro, Pedro Costa, F. Marques, Manuel Parente
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引用次数: 0

摘要

深度学习是机器学习的一个子集,它允许计算机以人类水平的表现执行某些任务,例如图像或视频识别。然而,深度模型需要大量的数据来学习,这就要求专家们把时间花在重复和不可扩展的标记数据集的任务上。主动学习表明,如果允许模型明智地选择要标记的最佳数据样本,则可以将注释的成本降至最低。因此,我们提出了一种深度和主动的学习方法,旨在最大限度地减少标记工作,同时最大限度地提高模型对特定任务的性能。我们提出了在远程操作车辆(ROV)视频中检测鱼类的任务,作为一个现实世界的问题,我们的框架可以成功地应用于其中。首先,我们证明主动学习优于随机抽样,随机抽样是构建数据集的最简单方法。此外,我们研究了针对给定任务的几种主动学习设置,即不同的获取和聚合函数。最后,所提出的方法仅使用19%的可用数据就能实现最佳的鱼类检测性能,从而将构建鱼类数据集的成本降低了80%以上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reducing Costs in Building a Dataset Using Deep and Active Learning
Deep learning, a subset of machine learning, allows computers to perform certain tasks, such as image or video recognition, with human level performance. However, deep models need huge amounts of data to learn from, which requires that experts spend their time in the repetitive and non-scalable task of labelling datasets. Active learning suggests that one can minimize the cost of annotation if a model is allowed to smartly choose the best data samples to be labelled. Therefore, we propose a deep and active learning approach that aims to minimize the labelling effort while maximizing the performance of a model for a certain task. We present the task of detecting fish in Remote Operated Vehicles (ROV) videos as a real world problem in which our framework can be successfully applied. To start with, we demonstrate that active learning outperforms random sampling, which is the simplest approach for building a dataset. Besides, we study several active learning settings for the given task, namely different acquisition and aggregation functions. Finally, the proposed methodology is shown to achieve top performance in detecting fish by using only 19% of the available data, thus reducing the cost of building our fish dataset by more than 80%.
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