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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.

Abstract-Recent studies show that concept-based approaches to opinion mining perform better than more canonical methods based on keyword spotting or word co-occurrence frequencies. SenticNet 1.0 is one of the most widely used publicly available resources for concept-based opinion mining. It gives polarity scores for a large number of single-and multi-word common sense concepts. However, developing high-quality opinion mining and sentiment analysis systems also requires affective information associated with the concepts. In this work, we present a methodology for enriching SenticNet concepts with affective information by assigning to them an emotion label. The created resource is freely available for academic use.

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