3 results about "Concentration parameter" patented technology

The invention particularly relates to a target classification and uncertainty evaluation method based on semantic association evidence fusion, and the method comprises the following steps: 1, constructing and training a target fusion classification neural network, and calculating a Dirichlet distribution concentration parameter of each modal input data representing an evidence quantity; step 2, associating the generated Dirichlet concentration parameter with the evidence quantity, and calculating single-mode uncertainty; step 3, constructing a semantic association matrix and performing discount correction, exploring potential association and confusion relationships among different categories, and completing multi-source evidence fusion through a Dempster combination rule; step 4, integrating the global uncertainty of the fused evidence and the local uncertainty of the single mode to obtain final uncertainty evaluation; according to the method, the modal information can be effectively fused to obtain high-precision fusion classification, the uncertainty of fusion classification can be quantitatively evaluated, a basis is provided for improving the safety and interpretability of intelligent classification decision, and the method is suitable for the multi-source sensor decision fusion field of automatic driving, medical diagnosis and the like.

The present application provides a prototype evidence sequence regression data processing method and system based on geometric ordering manifold learning, comprising: constructing a unit hypersphere feature space; based on the unit hypersphere feature space, constructing a learnable prototype vector; calculating the Euclidean distance between the feature vector and the learnable prototype vector to obtain an evidence vector; mapping the evidence vector to the concentration parameter of the Dirichlet distribution, combining the Dempster-Shafer theory reasoning framework to obtain a prototype evidence network model, and outputting a prediction result; based on the prediction result, introducing a ordinal manifold constraint based on the bulldozer distance, combining the Bayesian evidence loss to construct a loss function; based on the trained prototype evidence network model, processing new input data to obtain a final prediction result. The present application not only realizes accurate prediction of ordered data and geometric interpretability of feature space, but also has automatic rejection ability for out-of-distribution samples, significantly improving the robustness of the model in long-tail distribution and high-risk scenarios.

The application discloses a distribution-out detection method and device based on decoupling evidence deep learning and a medium, comprising: constructing a classification network based on evidence deep learning, modeling the output of the neural network as a concentration parameter of Dirichlet distribution; designing a decoupling training objective function, independently constraining the total amount of evidence and the category distribution form of the in-distribution samples and the out-of-distribution samples respectively, optimizing the model parameters by minimizing the difference between the predicted distribution and the target Dirichlet distribution; in the inference stage, calculating the differential entropy of the predicted Dirichlet distribution based on the concentration parameter of the network output, and taking it as an uncertainty measurement index for distribution-out detection. The application solves the problem that the weak feature in-distribution samples and the strong feature out-of-distribution samples are difficult to distinguish in the feature space in the traditional method by decoupling the correlation between the evidence amplitude and the distribution uncertainty, and effectively improves the distribution-out detection performance in the general image classification scene.