  We consider the two related problems of detecting if an example is
misclassified or out-of-distribution. We present a simple baseline that
utilizes probabilities from softmax distributions. Correctly classified
examples tend to have greater maximum softmax probabilities than erroneously
classified and out-of-distribution examples, allowing for their detection. We
assess performance by defining several tasks in computer vision, natural
language processing, and automatic speech recognition, showing the
effectiveness of this baseline across all. We then show the baseline can
sometimes be surpassed, demonstrating the room for future research on these
underexplored detection tasks.
