Out-of-Distribution Image Detection Using the Normalized Compression Distance

On detection of the out-of-distribution images, whose underlying distribution is different from that of the training dataset, we tackle to apply out-of-distribution detection methods to already deployed convolutional neural networks. Most recent approaches have to utilize out-of-distribution samples for validation or retrain the model, which makes it less practical for real-world applications. We propose a novel out-of-distribution detection method MALCOM, which neither uses any out-of-distribution samples nor retrain the model. Inspired by the method using the global average pooling on the feature maps of the convolutional neural networks, the goal of our method is to extract informative sequential patterns from the feature maps. To this end, we introduce a similarity metric which focuses on the shared patterns between two sequences. In short, MALCOM uses both the global average and spatial pattern of the feature maps to accurately identify out-of-distribution samples.