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 this https URL.

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。