There exist growing interests in intelligent systems for numerous medical imaging, image processing, and computer vision applications, such as face recognition, medical diagnosis, character recognition, and self-driving cars, among others. These applications usually require solving complex classification problems involving complex images with unknown data generative processes. In addition to recent successes of the current classification approaches relying on feature engineering and deep learning, several shortcomings of them, such as the lack of robustness, generalizability, and interpretability, have also been observed. These methods often require extensive training data, are computationally expensive, and are vulnerable to out-of-distribution samples, e.g., adversarial attacks. Recently, an accurate, data-efficient, computationally efficient, and robust transport-based classification approach has been proposed, which describes a generative model-based problem formulation and closed-form solution for a specific category of classification problems. However, all these approaches lack mechanisms to detect test samples outside the class distributions used during training. In real-world settings, where the collected training samples are unable to exhaust or cover all classes, the traditional classification schemes are unable to handle the unseen classes effectively, which is especially an important issue for safety-critical systems, such as self-driving and medical imaging diagnosis. In this work, we propose a method for detecting out-of-class distributions based on the distribution of sliced-Wasserstein distance from the Radon Cumulative Distribution Transform (R-CDT) subspace. We tested our method on the MNIST and two medical image datasets and reported better accuracy than the state-of-the-art methods without an out-of-class distribution detection procedure.
翻译:除其他外,这些应用通常需要解决复杂的分类问题,涉及复杂的图像,具有未知的数据基因化过程。除了目前依靠特征工程和深层次学习的分类方法最近取得的成功外,还观察到其中的一些缺点,例如缺乏稳健性、普遍性和可解释性。这些方法往往需要广泛的培训数据,计算成本昂贵,并且容易受到分配之外的样本,例如对抗性攻击。最近,提出了精确、准确、数据效率高、计算高效和稳健的基于运输的分类方法,其中描述了基于基因化模型的问题提法和针对特定类别分类问题的封闭式解决办法。然而,所有这些方法都缺乏在培训期间使用的班级分发之外检测测试样品的机制。在现实世界环境中,收集的培训样本无法耗尽或覆盖所有类别,传统的分类方案无法有效地处理可视课程,例如对抗性攻击。最近,提出了一种精确、高效、计算高效、基于计算高效和基于运输的分类方法,其中说明了基于基因模型的模型的配置和封闭式方法,这是我们用于检测更高水平的机级内部诊断方法。我们用一种更精确的机级分类方法,用以测测测测测算出一种自我测算。