Out-of-Distribution Detection using Multiple Semantic Label Representations
This addresses the problem of unreliable neural network predictions for out-of-distribution data, which is critical for real-world deployment, though it appears incremental.
The paper tackles out-of-distribution detection in neural networks by using multiple semantic dense label representations, and results show it compares favorably with previous methods on computer vision and speech tasks, also aiding in detecting misclassified and adversarial examples.
Deep Neural Networks are powerful models that attained remarkable results on a variety of tasks. These models are shown to be extremely efficient when training and test data are drawn from the same distribution. However, it is not clear how a network will act when it is fed with an out-of-distribution example. In this work, we consider the problem of out-of-distribution detection in neural networks. We propose to use multiple semantic dense representations instead of sparse representation as the target label. Specifically, we propose to use several word representations obtained from different corpora or architectures as target labels. We evaluated the proposed model on computer vision, and speech commands detection tasks and compared it to previous methods. Results suggest that our method compares favorably with previous work. Besides, we present the efficiency of our approach for detecting wrongly classified and adversarial examples.