GROOD: GRadient-aware Out-Of-Distribution detection in interpolated manifolds

• URL: http://arxiv.org/abs/2312.14427v1
• Date: Fri, 22 Dec 2023 04:28:43 GMT
• Title: GROOD: GRadient-aware Out-Of-Distribution detection in interpolated manifolds
• Authors: Mostafa ElAraby, Sabyasachi Sahoo, Yann Pequignot, Paul Novello, Liam Paull
• Abstract summary: Out-of-distribution detection in deep neural networks (DNNs) can pose risks in real-world deployments. We introduce GRadient-aware Out-Of-Distribution detection in. internative manifold (GROOD), a novel framework that relies on the discriminative power of gradient space. We show that GROD surpasses the established robustness of state-of-the-art baselines.
• Score: 12.727088216619386
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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