论文标题
Metamax：通过Weibull校准改进了开放式深度神经网络
MetaMax: Improved Open-Set Deep Neural Networks via Weibull Calibration
论文作者
论文摘要
开放式识别是指在推理时间出现训练期间未见课程的问题。这就需要能够识别新型类别的实例，同时保持闭和分类的歧视能力。 OpenMax是通过校准标准封闭式分类网络的预测分数来解决开放式识别的第一种深度神经网络方法。在本文中，我们介绍了MetAmax，这是一种更有效的后处理技术，通过直接建模类激活向量来改善当代方法。 MetAmax消除了计算类平均激活向量（MAV）的需求，以及在OpenMax中要求的查询图像和类MAV之间的距离。实验结果表明，MetAmax优于OpenMax，并且性能与其他最先进的方法相当。
Open-set recognition refers to the problem in which classes that were not seen during training appear at inference time. This requires the ability to identify instances of novel classes while maintaining discriminative capability for closed-set classification. OpenMax was the first deep neural network-based approach to address open-set recognition by calibrating the predictive scores of a standard closed-set classification network. In this paper we present MetaMax, a more effective post-processing technique that improves upon contemporary methods by directly modeling class activation vectors. MetaMax removes the need for computing class mean activation vectors (MAVs) and distances between a query image and a class MAV as required in OpenMax. Experimental results show that MetaMax outperforms OpenMax and is comparable in performance to other state-of-the-art approaches.
