论文标题
通过基于模型的神经网络的表征来检测新颖性
Novelty Detection Through Model-Based Characterization of Neural Networks
论文作者
论文摘要
在本文中，我们提出了一种基于模型的神经网络的表征，以检测新的输入类型和条件。新颖性检测对于确定可能会大大降低机器学习算法的性能的异常输入至关重要。大多数现有研究都集中在基于激活的表示上，以检测异常输入，这从数据角度限制了异常的表征。但是，就新颖性和异常而言，模型的观点也可以提供信息。为了阐明模型观点在新颖性检测中的重要性，我们利用了反向传播的梯度。我们进行了全面的分析，以比较梯度的表示能力与激活的能力，并表明梯度的表现优于新型类别和条件检测中的激活。我们使用四个图像识别数据集验证我们的方法，包括MNIST，Fashion-Mnist，CIFAR-10和CURE-TSR。我们在所有四个数据集上取得了显着改进，平均AUROC分别为0.953、0.918、0.582和0.746。
In this paper, we propose a model-based characterization of neural networks to detect novel input types and conditions. Novelty detection is crucial to identify abnormal inputs that can significantly degrade the performance of machine learning algorithms. Majority of existing studies have focused on activation-based representations to detect abnormal inputs, which limits the characterization of abnormality from a data perspective. However, a model perspective can also be informative in terms of the novelties and abnormalities. To articulate the significance of the model perspective in novelty detection, we utilize backpropagated gradients. We conduct a comprehensive analysis to compare the representation capability of gradients with that of activation and show that the gradients outperform the activation in novel class and condition detection. We validate our approach using four image recognition datasets including MNIST, Fashion-MNIST, CIFAR-10, and CURE-TSR. We achieve a significant improvement on all four datasets with an average AUROC of 0.953, 0.918, 0.582, and 0.746, respectively.
