It is often desirable to be able to recognize when inputs to a recognition function learned in a supervised manner correspond to classes unseen at training time. With this ability, new class labels could be assigned to these inputs by a human operator, allowing them to be incorporated into the recognition function-ideally under an efficient incremental update mechanism. While good algorithms that assume inputs from a fixed set of classes exist, e.g., artificial neural networks and kernel machines, it is not immediately obvious how to extend them to perform incremental learning in the presence of unknown query classes. Existing algorithms take little to no distributional information into account when learning recognition functions and lack a strong theoretical foundation. We address this gap by formulating a novel, theoretically sound classifier-the Extreme Value Machine (EVM). The EVM has a well-grounded interpretation derived from statistical Extreme Value Theory (EVT), and is the first classifier to be able to perform nonlinear kernel-free variable bandwidth incremental learning. Compared to other classifiers in the same deep network derived feature space, the EVM is accurate and efficient on an established benchmark partition of the ImageNet dataset.

中文翻译：

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极值机器


通常希望能够识别以监督方式学习的识别函数的输入何时对应于训练时未见过的类。有了这种能力，新的类标签可以由人类操作员分配给这些输入，从而使它们能够合并到识别功能中——理想情况下是在有效的增量更新机制下。虽然存在假设来自一组固定类的输入的良好算法，例如人工神经网络和内核机，但如何扩展它们以在存在未知查询类的情况下执行增量学习并不是立即显而易见的。现有算法在学习识别函数时很少甚至不考虑分布信息，并且缺乏坚实的理论基础。我们通过制定一种新颖的、理论上合理的分类器——极值机（EVM）来解决这一差距。 EVM 具有源自统计极值理论 (EVT) 的有根据的解释，并且是第一个能够执行非线性无核可变带宽增量学习的分类器。与同一深度网络派生特征空间中的其他分类器相比，EVM 在 ImageNet 数据集的既定基准分区上准确且高效。