We propose a novel confidence scoring mechanism for deep neural networks
based on a two-model paradigm involving a base model and a meta-model. The
confidence score is learned by the meta-model observing the base model
succeeding/failing at its task. As features to the meta-model, we investigate
linear classifier probes inserted between the various layers of the base model.
Our experiments demonstrate that this approach outperforms various baselines in
a filtering task, i.e., task of rejecting samples with low confidence.
Experimental results are presented using CIFAR-10 and CIFAR-100 dataset with
and without added noise. We discuss the importance of confidence scoring to
bridge the gap between experimental and real-world applications.