Exploring Vicinal Risk Minimization for Lightweight Out-of-Distribution Detection
This work addresses the problem of deploying OoD detection on edge devices for practitioners by developing a method with minimal overhead.
This paper explores the application of Vicinal Risk Minimization (VRM) to improve out-of-distribution (OoD) detection in deep neural networks. The authors developed a lightweight OoD detector using VRM, achieving near-competitive performance with significantly reduced compute and memory overhead compared to existing methods.
Deep neural networks have found widespread adoption in solving complex tasks ranging from image recognition to natural language processing. However, these networks make confident mispredictions when presented with data that does not belong to the training distribution, i.e. out-of-distribution (OoD) samples. In this paper we explore whether the property of Vicinal Risk Minimization (VRM) to smoothly interpolate between different class boundaries helps to train better OoD detectors. We apply VRM to existing OoD detection techniques and show their improved performance. We observe that existing OoD detectors have significant memory and compute overhead, hence we leverage VRM to develop an OoD detector with minimal overheard. Our detection method introduces an auxiliary class for classifying OoD samples. We utilize mixup in two ways to implement Vicinal Risk Minimization. First, we perform mixup within the same class and second, we perform mixup with Gaussian noise when training the auxiliary class. Our method achieves near competitive performance with significantly less compute and memory overhead when compared to existing OoD detection techniques. This facilitates the deployment of OoD detection on edge devices and expands our understanding of Vicinal Risk Minimization for use in training OoD detectors.