Out-of-distribution (OOD) detection is an important task to ensure the reliability and safety of deep learning and the discriminator models outperform others for now. However, the feature extraction of the discriminator models must compress the data and lose certain information, leaving room for bad cases and malicious attacks. In this paper, we provide a new assumption that the discriminator models are more sensitive to some subareas of the input space and such perceptron bias causes bad cases and overconfidence areas. Under this assumption, we design new detection methods and indicator scores. For detection methods, we introduce diffusion models (DMs) into OOD detection. We find that the diffusion denoising process (DDP) of DMs also functions as a novel form of asymmetric interpolation, which is suitable to enhance the input and reduce the overconfidence areas. For indicator scores, we find that the features of the discriminator models of OOD inputs occur sharp changes under DDP and use the norm of this dynamic change as our indicator scores. Therefore, we develop a new framework to combine the discriminator and generation models to do OOD detection under our new assumption. The discriminator models provide proper detection spaces and the generation models reduce the overconfidence problem. According to our experiments on CIFAR10 and CIFAR100, our methods get competitive results with state-of-the-art methods. Our implementation is available at https://github.com/luping-liu/DiffOOD.

本文提出了一种新的OOC检测方法, 探讨了鉴别模型在特定区域中对数据更敏感, 借此提出了基于扩散模型的检测方法和指标分数, 结合鉴别模型和生成模型进行检测, 实验结果表明该方法在CIFAR10和CIFAR100数据集上有竞争力。

Perceptron偏置的扩散去噪处理在超分布检测中的应用