Zero-shot Object-Level OOD Detection with Context-Aware Inpainting
CoRR(2024)
摘要
Machine learning algorithms are increasingly provided as black-box cloud
services or pre-trained models, without access to their training data. This
motivates the problem of zero-shot out-of-distribution (OOD) detection.
Concretely, we aim to detect OOD objects that do not belong to the classifier's
label set but are erroneously classified as in-distribution (ID) objects. Our
approach, RONIN, uses an off-the-shelf diffusion model to replace detected
objects with inpainting. RONIN conditions the inpainting process with the
predicted ID label, drawing the input object closer to the in-distribution
domain. As a result, the reconstructed object is very close to the original in
the ID cases and far in the OOD cases, allowing RONIN to effectively
distinguish ID and OOD samples. Throughout extensive experiments, we
demonstrate that RONIN achieves competitive results compared to previous
approaches across several datasets, both in zero-shot and non-zero-shot
settings.
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