论文标题
具有基于梯度表示的开放式识别
Open-Set Recognition with Gradient-Based Representations
论文作者
论文摘要
用于图像分类任务的神经网络假定推理期间的任何给定图像属于其中一个培训类别。在模型可能遇到未知类别的输入的现实应用程序中，这种封闭设置的假设受到挑战。开放式识别旨在通过正确对已知类别进行分类，通过拒绝未知类来解决此问题。在本文中，我们建议利用从已知分类器获得的基于梯度的表示，以仅通过已知类别的实例来训练未知的检测器。渐变对应于正确表示给定样本所需的模型更新量，我们利用该模型更新以了解模型具有其学术特征的输入的能力。我们的方法可以使用以有监督的方式对已知类别进行培训的任何分类器使用，而无需明确地对未知样本的分布进行建模。我们表明，我们的基于梯度的方法在开放式分类中优于最先进的方法，高达11.6％。
Neural networks for image classification tasks assume that any given image during inference belongs to one of the training classes. This closed-set assumption is challenged in real-world applications where models may encounter inputs of unknown classes. Open-set recognition aims to solve this problem by rejecting unknown classes while classifying known classes correctly. In this paper, we propose to utilize gradient-based representations obtained from a known classifier to train an unknown detector with instances of known classes only. Gradients correspond to the amount of model updates required to properly represent a given sample, which we exploit to understand the model's capability to characterize inputs with its learned features. Our approach can be utilized with any classifier trained in a supervised manner on known classes without the need to model the distribution of unknown samples explicitly. We show that our gradient-based approach outperforms state-of-the-art methods by up to 11.6% in open-set classification.
