无需人工标注反馈，实时学习新面孔识别模型
Online semi-supervised perception: Real-time learning without explicit feedback
- 构建动态图结构，用在线无标签数据持续优化离线标注的初始模型
- 在3个复杂视频数据集上实现更高精度与召回率，支持实时运行
- 适用于需要持续学习、缺乏即时反馈的视觉识别场景
本文提出一种无需显式反馈的实时学习算法，结合图上的半监督学习与在线学习思想。算法通过迭代构建世界图表示，并用在线观测样本更新该表示。已标注样本作为初始偏差离线提供，随后通过持续收集的无标签样本流来更新这一偏差。我们论证了该算法的合理性，讨论了高效实现方法，证明了其解的质量存在可证明的后悔界，并应用于实时人脸识別任务。所提识别器可实时运行，在3个具有挑战性的视频数据集上均取得更优的精确率与召回率。
原文摘要 · Abstract (English)
This paper proposes an algorithm for real-time learning without explicit feedback. The algorithm combines the ideas of semi-supervised learning on graphs and online learning. In particular, it iteratively builds a graphical representation of its world and updates it with observed examples. Labeled examples constitute the initial bias of the algorithm and are provided offline, and a stream of unlabeled examples is collected online to update this bias. We motivate the algorithm, discuss how to implement it efficiently, prove a regret bound on the quality of its solutions, and apply it to the problem of real-time face recognition. Our recognizer runs in real time, and achieves superior precision and recall on 3 challenging video datasets.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。