  Out-of-distribution (OOD) detection is crucial for deploying robust machine
learning models, especially in areas where security is critical. However,
traditional OOD detection methods often fail to capture complex data
distributions from large scale date. In this paper, we present a novel approach
for OOD detection that leverages the generative ability of diffusion models and
the powerful feature extraction capabilities of CLIP. By using these features
as conditional inputs to a diffusion model, we can reconstruct the images after
encoding them with CLIP. The difference between the original and reconstructed
images is used as a signal for OOD identification. The practicality and
scalability of our method is increased by the fact that it does not require
class-specific labeled ID data, as is the case with many other methods.
Extensive experiments on several benchmark datasets demonstrates the robustness
and effectiveness of our method, which have significantly improved the
detection accuracy.
