A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
This work addresses a critical issue for deploying reliable AI systems by providing a foundational baseline for detection tasks, though it is incremental as it builds on existing softmax-based methods.
The paper tackles the problem of detecting misclassified and out-of-distribution examples in neural networks by proposing a simple baseline using maximum softmax probabilities, showing its effectiveness across computer vision, natural language processing, and automatic speech recognition tasks.
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.