Confidence Scoring Using Whitebox Meta-models with Linear Classifier ProbesDownload PDF

02 Nov 2017 (modified: 25 Jan 2018)ICLR 2018 Conference Withdrawn SubmissionReaders: Everyone
Abstract: We propose a confidence scoring mechanism for multi-layer neural networks based on a paradigm of a base model and a meta-model. The confidence score is learned by the meta-model using features derived from the base model – a deep neural network considered a whitebox. As features, we investigate linear classifier probes inserted between the various layers of the base model and trained using each layer’s intermediate activations. Experiments show 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 exploring various aspects of the method.
Keywords: confidence scoring, meta-model, linear classifier probes
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