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Found 8 papers, 2 papers with code
Adaptive Temperature Scaling for Robust Calibration of Deep Neural Networks
We show that when there is plenty of data complex models like neural networks yield better performance, but are prone to fail when the amount of data is limited, a common situation in certain post-hoc calibration applications like medical diagnosis.
BiosecurID: a multimodal biometric database
A new multimodal biometric database, acquired in the framework of the BiosecurID project, is presented together with the description of the acquisition setup and protocol.
On Calibration of Mixup Training for Deep Neural Networks
Data Augmentation (DA) strategies have been proposed to regularize these models, being Mixup one of the most popular due to its ability to improve the accuracy, the uncertainty quantification and the calibration of DNN.
Bayesian Strategies for Likelihood Ratio Computation in Forensic Voice Comparison with Automatic Systems
This paper explores several strategies for Forensic Voice Comparison (FVC), aimed at improving the performance of the LRs when using generative Gaussian score-to-LR models.
Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks
Deep Neural Networks (DNNs) have achieved state-of-the-art accuracy performance in many tasks.
Generative Models For Deep Learning with Very Scarce Data
The goal of this paper is to deal with a data scarcity scenario where deep learning techniques use to fail.
Improving Automated Latent Fingerprint Identification using Extended Minutia Types
We report significant improvements in the rank identification accuracies when these minutiae matchers are augmented with our proposed algorithm based on rare minutiae features.
Offline Deep models calibration with bayesian neural networks
We apply Bayesian Neural Networks to improve calibration of state-of-the-art deep neural networks.
