论文标题
从开放式视频面部识别中从低标记的流数据中学习的增量学习
Incremental Learning from Low-labelled Stream Data in Open-Set Video Face Recognition
论文作者
论文摘要
深度学习的方法为一般分类问题带来了解决方案，并提供了大量带注释的数据进行培训的问题。相比之下，在不断学习一组非平稳类的过程中，取得的进展较小，主要是应用于流媒体数据无监督问题时。 在这里，我们提出了一种新颖的增量学习方法，该方法将深层特征编码器与SVM的开放式动态合奏结合在一起，以解决从流式面部数据中识别感兴趣的个人（IOI）的问题。从在几个视频框架上训练的简单弱分类器中，我们的方法可以使用无监督的操作数据来增强识别。我们的方法适应了避免灾难性遗忘的新模式，并部分康复了。此外，为了更好地遵守现实世界的条件，该系统旨在在开放式设置中运行。结果表明，对非适应性最新方法的F1得分增加了15％的好处。
Deep Learning approaches have brought solutions, with impressive performance, to general classification problems where wealthy of annotated data are provided for training. In contrast, less progress has been made in continual learning of a set of non-stationary classes, mainly when applied to unsupervised problems with streaming data. Here, we propose a novel incremental learning approach which combines a deep features encoder with an Open-Set Dynamic Ensembles of SVM, to tackle the problem of identifying individuals of interest (IoI) from streaming face data. From a simple weak classifier trained on a few video-frames, our method can use unsupervised operational data to enhance recognition. Our approach adapts to new patterns avoiding catastrophic forgetting and partially heals itself from miss-adaptation. Besides, to better comply with real world conditions, the system was designed to operate in an open-set setting. Results show a benefit of up to 15% F1-score increase respect to non-adaptive state-of-the-art methods.
