论文标题
辅助生活应用中半监督增量学习的基于CNN的功能空间
A CNN-based Feature Space for Semi-supervised Incremental Learning in Assisted Living Applications
论文作者
论文摘要
卷积神经网络（CNN）有时会面临着不断变化的外观（新实例）的对象，这些对象超出了其概括能力。这要求CNN合并新知识，即逐步学习。在本文中，我们在辅助生活的背景下关注这个问题。我们建议使用从训练数据集导致的特征空间来自动标记有问题的图像，而CNN无法正确识别。这个想法是利用特征空间中的额外信息进行半监督标签，并采用有问题的图像来改善CNN的分类模型。除其他好处外，所得的半监督增量学习过程允许将新实例的分类精度提高40％，如广泛的实验所示。
A Convolutional Neural Network (CNN) is sometimes confronted with objects of changing appearance ( new instances) that exceed its generalization capability. This requires the CNN to incorporate new knowledge, i.e., to learn incrementally. In this paper, we are concerned with this problem in the context of assisted living. We propose using the feature space that results from the training dataset to automatically label problematic images that could not be properly recognized by the CNN. The idea is to exploit the extra information in the feature space for a semi-supervised labeling and to employ problematic images to improve the CNN's classification model. Among other benefits, the resulting semi-supervised incremental learning process allows improving the classification accuracy of new instances by 40% as illustrated by extensive experiments.
