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We consider the two related problems of detecting if an example ismisclassified or out-of-distribution. We present a simple baseline thatutilizes probabilities from softmax distributions. Correctly classifiedexamples tend to have greater maximum softmax probabilities than erroneouslyclassified and out-of-distribution examples, allowing for their detection. Weassess performance by defining several tasks in computer vision, naturallanguage processing, and automatic speech recognition, showing theeffectiveness of this baseline across all. We then show the baseline cansometimes be surpassed, demonstrating the room for future research on theseunderexplored detection tasks.

A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks