NODI: Out-Of-Distribution Detection with Noise from Diffusion
CoRR(2024)
摘要
Out-of-distribution (OOD) detection is a crucial part of deploying machine
learning models safely. It has been extensively studied with a plethora of
methods developed in the literature. This problem is tackled with an OOD score
computation, however, previous methods compute the OOD scores with limited
usage of the in-distribution dataset. For instance, the OOD scores are computed
with information from a small portion of the in-distribution data. Furthermore,
these methods encode images with a neural image encoder. The robustness of
these methods is rarely checked with respect to image encoders of different
training methods and architectures. In this work, we introduce the diffusion
process into the OOD task. The diffusion model integrates information on the
whole training set into the predicted noise vectors. What's more, we deduce a
closed-form solution for the noise vector (stable point). Then the noise vector
is converted into our OOD score, we test both the deep model predicted noise
vector and the closed-form noise vector on the OOD benchmarks .
Our method outperforms previous OOD methods across all types of image encoders
(Table. ). A 3.5% performance gain is achieved with the MAE-based
image encoder. Moreover, we studied the robustness of OOD methods by applying
different types of image encoders. Some OOD methods failed to generalize well
when switching image encoders from ResNet to Vision Transformers, our method
performs exhibits good robustness with all the image encoders.
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