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

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Authors Kevin Gimpel, Dan Hendrycks
Journal/Conference Name 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings
Paper Category
Paper Abstract 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.
Date of publication 2016
Code Programming Language Multiple
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