COC curve: operating neural networks at high accuracy and low manual effort

Sara Sangalli, Ertunc Erdil, Ender Konukoglu

Abstract:

In human-AI collaboration systems for critical applications based on neural networks, humans should set an operating point based on a model's confidence to determine when the decision should be delegated to experts.
The underlying assumption is that the network's confident predictions are also correct.
However, modern neural networks are notoriously overconfident in their predictions, thus they achieve lower accuracy even when operated at high confidence. Network calibration methods mitigate this problem by encouraging models to make predictions whose confidence is consistent with the accuracy, i.e., encourage confidence to reflect the number of mistakes the network is expected to make. However, they do not consider that data need to be manually analysed by experts in critical applications if the confidence of the network is below a certain level. This can be crucial for applications where available expert time is limited and expensive, e.g., medical ones.
In this paper, we propose (1) Confidence Operating Characteristics (COC) curve that assesses a predictive model in terms of accuracy and manual analysis it requires for varying operating points on confidence, and (2) a new loss function for classification that takes into account both aspects and derived from the COC curve.
We perform extensive experiments on multiple computer vision and medical image datasets for classification and compare the proposed approach with the existing network calibration methods. Our results demonstrate that our method improves classification accuracy while delegating less number of decisions to human experts, achieves better out-of-distribution samples detection and on par calibration performance compared to existing methods.

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