Out-of-Distribution (OoD) detection is vital for the reliability of Deep Neural Networks (DNNs).

One key challenge in Out-of-Distribution (OOD) detection is the absence of ground-truth OOD samples during training.

Consequently, we investigate how attribution gradients lead to uncertain explanation outcomes and introduce two forms of abnormalities for OOD detection: the zero-deflation abnormality and the channel-wise average abnormality.

However, in certain specialized domains, such as healthcare or harmless content generation, it is nearly impossible to obtain a large volume of high-quality data that matches the downstream distribution.

Uncertainty estimation aims to evaluate the confidence of a trained deep neural network.