Shypd.aiThe largest open index of AI toolspytorch-classification-uncertainty is an Open Source & Models tool that provides a PyTorch implementation of "Evidential Deep Learning to Quantify Classification Uncertainty." It offers an easy-to-run demo with low computational requirements for quantifying classification uncertainty.
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This repository offers a PyTorch implementation of the paper "Evidential Deep Learning to Quantify Classification Uncertainty." It provides an accessible and easy-to-run demonstration of the concepts presented in the paper, requiring low computational resources. The tool allows users to explore how neural networks can be trained to quantify their prediction confidence, moving beyond standard softmax probabilities. It includes implementations for various loss functions, such as Expected Mean Square Error, Expected Cross Entropy, and Negative Log of the Expected Likelihood, enabling different approaches to uncertainty estimation. The project highlights how this method improves uncertainty detection for out-of-distribution queries and enhances resilience against adversarial perturbations, making it valuable for researchers and developers working on robust AI models.
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