Title: Detection and Mitigation of Rare Subclasses in Deep Neural Network Classifiers Show Chinese title

Title: 深度神经网络分类器中罕见子类的检测与缓解

Abstract: Regions of high-dimensional input spaces that are underrepresented in training datasets reduce machine-learnt classifier performance, and may lead to corner cases and unwanted bias for classifiers used in decision making systems. When these regions belong to otherwise well-represented classes, their presence and negative impact are very hard to identify. We propose an approach for the detection and mitigation of such rare subclasses in deep neural network classifiers. The new approach is underpinned by an easy-to-compute commonality metric that supports the detection of rare subclasses, and comprises methods for reducing the impact of these subclasses during both model training and model exploitation. We demonstrate our approach using two well-known datasets, MNIST's handwritten digits and Kaggle's cats/dogs, identifying rare subclasses and producing models which compensate for subclass rarity. In addition we demonstrate how our run-time approach increases the ability of users to identify samples likely to be misclassified at run-time. Show Chinese abstract

Abstract: 训练数据集中代表性不足的高维输入空间区域会降低机器学习分类器的性能，并可能导致决策系统中使用的分类器出现corner case和不必要的偏差。 当这些区域属于其他方面表现良好的类别时，其存在及其负面影响非常难以识别。 我们提出了一种用于检测和缓解深度神经网络分类器中此类稀有子类的方法。 新方法基于一个易于计算的共同性度量指标，该指标支持稀有子类的检测，并包括在模型训练和模型利用期间减少这些子类影响的方法。 我们使用两个著名的数据集（MNIST的手写数字和Kaggle的猫狗数据集）演示了我们的方法，识别稀有子类并生成补偿子类稀有性的模型。 此外，我们展示了我们的运行时方法如何提高用户在运行时识别可能被错误分类样本的能力。