论文标题
深度学习的单个动物识别系统的单口系统
The Sloop System for Individual Animal Identification with Deep Learning
论文作者
论文摘要
麻省理工学院单桅帆船系统索引并从非平稳动物种群分布的数据库中检索照片。为此，它使用专家和人群的稀疏相关性反馈自适应地表示并匹配通用的视觉功能表示。在这里，我们描述了单口系统及其应用，然后将其方法与标准深度学习公式进行比较。然后，我们证明具有振幅和变形特征的启动需要非常浅的网络才能产生卓越的识别结果。结果表明，使Sloop的高回报表现的相关反馈对于深度学习方法的个人识别方法也可能至关重要。
The MIT Sloop system indexes and retrieves photographs from databases of non-stationary animal population distributions. To do this, it adaptively represents and matches generic visual feature representations using sparse relevance feedback from experts and crowds. Here, we describe the Sloop system and its application, then compare its approach to a standard deep learning formulation. We then show that priming with amplitude and deformation features requires very shallow networks to produce superior recognition results. Results suggest that relevance feedback, which enables Sloop's high-recall performance may also be essential for deep learning approaches to individual identification to deliver comparable results.
