Fusion of evidential CNN classifiers for image classification
We propose an information-fusion approach based on belief functions to combine convolutional neural networks. In this approach, several pre-trained DS-based CNN architectures extract features from input images and convert them into mass functions on different frames of discernment. A fusion module then aggregates these mass functions using Dempster's rule. An end-to-end learning procedure allows us to fine-tune the overall architecture using a learning set with soft labels, which further improves the classification performance. The effectiveness of this approach is demonstrated experimentally using three benchmark databases.
Code (0)
등록된 구현이 없습니다.
Tasks
Classificationimage-classificationImage ClassificationSimilar Papers 제목 키워드 기반
Medical Image Segmentation with Belief Function Theory and Deep Learning
Deep learning has shown promising contributions in medical image segmentation with powerful learning and feature representation abilities. However, it has limitations for reasoning with and combining imperfect (imprecise…
Deep LearningImage SegmentationMedical Image SegmentationSegmentation+3Attribute Fusion-based Classifier on Framework of Belief Structure
Dempster-Shafer Theory (DST) provides a powerful framework for modeling uncertainty and has been widely applied to multi-attribute classification tasks. However, traditional DST-based attribute fusion-based classifiers s…
Evidential Uncertainty Sets in Deep Classifiers Using Conformal Prediction
In this paper, we propose Evidential Conformal Prediction (ECP) method for image classifiers to generate the conformal prediction sets. Our method is designed based on a non-conformity score function that has its roots i…
Conformal PredictionFusion of neural networks, for LIDAR-based evidential road mapping
LIDAR sensors are usually used to provide autonomous vehicles with 3D representations of their environment. In ideal conditions, geometrical models could detect the road in LIDAR scans, at the cost of a manual tuning of …
Autonomous VehiclesEnhancing Adaptive Deep Networks for Image Classification via Uncertainty-aware Decision Fusion
Handling varying computational resources is a critical issue in modern AI applications. Adaptive deep networks, featuring the dynamic employment of multiple classifier heads among different layers, have been proposed to …
Decision Makingimage-classificationImage Classification