Recently, Convolutional Neural Networks (CNNs) have shown unprecedented success in the field of computer vision, especially on challenging image classification tasks by relying on a universal approach, i.e., training a deep model on a massive dataset of supervised examples. While unlabeled data are often an abundant resource, collecting a large set of labeled data, on the other hand, are very expensive, which often require considerable human efforts. One way to ease out this is to effectively select and label highly informative instances from a pool of unlabeled data (i.e., active learning). This paper proposed a new method of batch-mode active learning, Dual Active Sampling(DAS), which is based on a simple assumption, if two deep neural networks (DNNs) of the same structure and trained on the same dataset give significantly different output for a given sample, then that particular sample should be picked for additional training. While other state of the art methods in this field usually require intensive computational power or relying on a complicated structure, DAS is simpler to implement and, managed to get improved results on Cifar-10 with preferable computational time compared to the core-set method.
翻译:最近,进化神经网络(CNNs)在计算机视觉领域表现出前所未有的成功,特别是在具有挑战性的图像分类任务方面,依靠一种通用的方法,即对大量受监督实例的数据集进行深层模型培训。虽然未贴标签的数据往往是丰富的资源,但收集大量标签数据的费用却非常昂贵,这往往需要大量的人力努力。缓解这种情况的方法之一是从一组未贴标签的数据(即积极学习)中有效地选择和标出高度信息化的案例。本文提出了一种分批模式主动学习的新方法,即双重主动抽样(DAS),这种方法基于一个简单的假设,即同一结构的两个深层神经网络(DNNS)与同一数据集的培训为特定样本提供了显著不同的输出,然后为进行额外的培训而选取特定样本。虽然该领域的其他艺术方法通常需要密集的计算能力或依赖复杂的结构,但DAS比较容易实施,并且设法改进了Cifar-10的结果,与核心设置方法相比,计算时间比较可取。