As deep learning methods form a critical part in commercially important applications such as autonomous driving and medical diagnostics, it is important to reliably detect out-of-distribution (OOD) inputs while employing these algorithms. In this work, we propose an OOD detection algorithm which comprises of an ensemble of classifiers. We train each classifier in a self-supervised manner by leaving out a random subset of training data as OOD data and the rest as in-distribution (ID) data. We propose a novel margin-based loss over the softmax output which seeks to maintain at least a margin m between the average entropy of the OOD and in-distribution samples. In conjunction with the standard cross-entropy loss, we minimize the novel loss to train an ensemble of classifiers. We also propose a novel method to combine the outputs of the ensemble of classifiers to obtain OOD detection score and class prediction. Overall, our method convincingly outperforms Hendrycks et al. [7] and the current state-of-the-art ODIN [13] on several OOD detection benchmarks.
Authors
Apoorv Vyas
Nataraj Jammalamadaka
Xia Zhu
Dipankar Das
Related Content
On Offline Evaluation of Vision-based Driving Models
Autonomous driving models should ideally be evaluated by deploying them on a fleet of physical vehicles in the real world...
Deep Learning under Privileged Information Using Heteroscedastic Dropout
Unlike machines, humans learn through rapid, abstract model-building. The role of a teacher is not simply to hammer home right...
Towards Understanding End-of-trip Instructions in a Taxi Ride...
We introduce a dataset containing human-authored descriptions of target locations in an "end-of-trip in a taxi ride" scenario. We describe...
Sparse DNNs with Improved Adversarial Robustness
Deep neural networks (DNNs) are computationally/memory-intensive and vulnerable to adversarial attacks, making them prohibitive in some real-world applications. By converting...
