论文标题
最佳拒绝功能符合字符识别任务
Optimal Rejection Function Meets Character Recognition Tasks
论文作者
论文摘要
在本文中，我们提出了一种通过拒绝函数拒绝模棱两可的样本的最佳排斥方法。在学习拒绝（LWR）的框架下，将使用该拒绝功能与分类功能一起训练。 LWR的亮点是：（1）拒绝策略不是启发式的，而是机器学习理论具有强大的背景，并且（2）可以在任意特征空间上训练拒绝功能，这与分类特征空间不同。后者建议我们可以选择一个更适合拒绝的特征空间。尽管过去对LWR的研究仅关注其理论方面，但我们建议将LWR用于实用的模式分类任务。此外，我们建议使用不同CNN层的特征进行分类和拒绝。我们对NotMnist分类和性格/非字符分类的广泛实验表明，所提出的方法比传统的拒绝策略获得了更好的性能。
In this paper, we propose an optimal rejection method for rejecting ambiguous samples by a rejection function. This rejection function is trained together with a classification function under the framework of Learning-with-Rejection (LwR). The highlights of LwR are: (1) the rejection strategy is not heuristic but has a strong background from a machine learning theory, and (2) the rejection function can be trained on an arbitrary feature space which is different from the feature space for classification. The latter suggests we can choose a feature space that is more suitable for rejection. Although the past research on LwR focused only on its theoretical aspect, we propose to utilize LwR for practical pattern classification tasks. Moreover, we propose to use features from different CNN layers for classification and rejection. Our extensive experiments of notMNIST classification and character/non-character classification demonstrate that the proposed method achieves better performance than traditional rejection strategies.
