GROOD: GRadient-aware Out-Of-Distribution detection in interpolated
  manifolds
Mostafa ElAraby Sabyasachi Sahoo Yann Pequignot Paul Novello Liam Paull
Deep neural networks (DNNs) often fail silently with over-confident
predictions on out-of-distribution (OOD) samples, posing risks in real-world
deployments. Existing techniques predominantly emphasize either the feature
representation space or the gradient norms computed with respect to DNN
parameters, yet they overlook the intricate gradient distribution and the
topology of classification regions. To address this gap, we introduce
GRadient-aware Out-Of-Distribution detection in interpolated manifolds (GROOD),
a novel framework that relies on the discriminative power of gradient space to
distinguish between in-distribution (ID) and OOD samples. To build this space,
GROOD relies on class prototypes together with a prototype that specifically
captures OOD characteristics. Uniquely, our approach incorporates a targeted
mix-up operation at an early intermediate layer of the DNN to refine the
separation of gradient spaces between ID and OOD samples. We quantify OOD
detection efficacy using the distance to the nearest neighbor gradients derived
from the training set, yielding a robust OOD score. Experimental evaluations
substantiate that the introduction of targeted input mix-upamplifies the
separation between ID and OOD in the gradient space, yielding impressive
results across diverse datasets. Notably, when benchmarked against ImageNet-1k,
GROOD surpasses the established robustness of state-of-the-art baselines.
Through this work, we establish the utility of leveraging gradient spaces and
class prototypes for enhanced OOD detection for DNN in image classification.