SR-OOD: Out-of-Distribution Detection via Sample Repairing
This addresses the reliability and robustness of machine learning models for applications requiring OOD detection, but it appears incremental as it builds on existing generative methods with a novel approach.
The paper tackled the problem of out-of-distribution (OOD) detection by proposing SR-OOD, a framework that uses sample repairing to reveal semantic inconsistencies, achieving superior performance over state-of-the-art generative methods on datasets like CIFAR-10, CelebA, and Pokemon.
Out-of-distribution (OOD) detection is a crucial task for ensuring the reliability and robustness of machine learning models. Recent works have shown that generative models often assign high confidence scores to OOD samples, indicating that they fail to capture the semantic information of the data. To tackle this problem, we take advantage of sample repairing and propose a novel OOD detection framework, namely SR-OOD. Our framework leverages the idea that repairing an OOD sample can reveal its semantic inconsistency with the in-distribution data. Specifically, our framework consists of two components: a sample repairing module and a detection module. The sample repairing module applies erosion to an input sample and uses a generative adversarial network to repair it. The detection module then determines whether the input sample is OOD using a distance metric. Our framework does not require any additional data or label information for detection, making it applicable to various scenarios. We conduct extensive experiments on three image datasets: CIFAR-10, CelebA, and Pokemon. The results demonstrate that our approach achieves superior performance over the state-of-the-art generative methods in OOD detection.