Mining and reasoning of data uncertainty-induced imprecision in deep image classification
暂无分享,去创建一个
[1] Wen Jiang,et al. A novel quantum model of mass function for uncertain information fusion , 2022, Inf. Fusion.
[2] Francisco J. Martínez-Murcia,et al. Uncertainty-driven ensembles of multi-scale deep architectures for image classification , 2022, Inf. Fusion.
[3] N. Xu,et al. Progressive Enhancement of Label Distributions for Partial Multilabel Learning , 2021, IEEE Transactions on Neural Networks and Learning Systems.
[4] Sheela Ramanna,et al. Multimodal Co-learning: Challenges, Applications with Datasets, Recent Advances and Future Directions , 2021, Inf. Fusion.
[5] Junjun Jiang,et al. Asymmetric Loss Functions for Learning with Noisy Labels , 2021, ICML.
[6] Yi Yang,et al. Learning With Noisy Labels via Self-Reweighting From Class Centroids , 2021, IEEE Transactions on Neural Networks and Learning Systems.
[7] Thierry Denoeux,et al. An evidential classifier based on Dempster-Shafer theory and deep learning , 2021, Neurocomputing.
[8] Xiangtao Zheng,et al. A Supervised Segmentation Network for Hyperspectral Image Classification , 2021, IEEE Transactions on Image Processing.
[9] Kuang Zhou,et al. Dynamic evidential clustering algorithm , 2021, Knowl. Based Syst..
[10] Julian Fierrez,et al. Fusing CNNs and statistical indicators to improve image classification , 2020, Inf. Fusion.
[11] Ivor W. Tsang,et al. The Emerging Trends of Multi-Label Learning , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[12] Li Liu,et al. A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges , 2020, Inf. Fusion.
[13] Bo Liu,et al. Incorporating DeepLabv3+ and object-based image analysis for semantic segmentation of very high resolution remote sensing images , 2020, Int. J. Digit. Earth.
[14] Feng Jiang,et al. Deep Learning and Dempster-Shafer Theory Based Insider Threat Detection , 2020 .
[15] Haibo He,et al. Symmetric All Convolutional Neural-Network-Based Unsupervised Feature Extraction for Hyperspectral Images Classification , 2020, IEEE Transactions on Cybernetics.
[16] Raviv Raich,et al. Incomplete Label Multiple Instance Multiple Label Learning , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[17] Hwanjun Song,et al. Learning From Noisy Labels With Deep Neural Networks: A Survey , 2020, IEEE Transactions on Neural Networks and Learning Systems.
[18] Peng Gao,et al. Reconstruction Regularized Deep Metric Learning for Multi-Label Image Classification , 2020, IEEE Transactions on Neural Networks and Learning Systems.
[19] Wai Keung Wong,et al. Joint Optimal Transport With Convex Regularization for Robust Image Classification , 2020, IEEE Transactions on Cybernetics.
[20] Hong Zhu,et al. Structured Dictionary Learning for Image Denoising Under Mixed Gaussian and Impulse Noise , 2020, IEEE Transactions on Image Processing.
[21] Bo Sun,et al. Emotion recognition model based on the Dempster–Shafer evidence theory , 2020, J. Electronic Imaging.
[22] Zhifeng Liu,et al. Standing-Posture Recognition in Human–Robot Collaboration Based on Deep Learning and the Dempster–Shafer Evidence Theory , 2020, Sensors.
[23] Kehua Guo,et al. iFusion: Towards efficient intelligence fusion for deep learning from real-time and heterogeneous data , 2019, Inf. Fusion.
[24] Willem Waegeman,et al. Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods , 2019, Machine Learning.
[25] Praveen Sankaran,et al. A Deep Neural Network Classifier Based on Belief Theory , 2019, CVIP.
[26] Xianming Liu,et al. Hyperspectral Image Classification in the Presence of Noisy Labels , 2018, IEEE Transactions on Geoscience and Remote Sensing.
[27] G. Yen,et al. Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification , 2018, IEEE Transactions on Cybernetics.
[28] Quan Pan,et al. Combination of Classifiers With Optimal Weight Based on Evidential Reasoning , 2018, IEEE Transactions on Fuzzy Systems.
[29] Mita Nasipuri,et al. Combining Multi-level Contexts of Superpixel using Convolutional Neural Networks to perform Natural Scene Labeling , 2018, Advances in Intelligent Systems and Computing.
[30] Gang Sun,et al. Squeeze-and-Excitation Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[31] Bo Du,et al. Robust and Discriminative Labeling for Multi-Label Active Learning Based on Maximum Correntropy Criterion , 2017, IEEE Transactions on Image Processing.
[32] Ivor W. Tsang,et al. Co-Labeling for Multi-View Weakly Labeled Learning , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[33] Clayton Scott,et al. A Rate of Convergence for Mixture Proportion Estimation, with Application to Learning from Noisy Labels , 2015, AISTATS.
[34] Quan Pan,et al. Median evidential c-means algorithm and its application to community detection , 2015, Knowl. Based Syst..
[35] Dacheng Tao,et al. Classification with Noisy Labels by Importance Reweighting , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[36] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[37] M. Verleysen,et al. Classification in the Presence of Label Noise: A Survey , 2014, IEEE Transactions on Neural Networks and Learning Systems.
[38] Haibo He,et al. Learning from Imbalanced Data , 2009, IEEE Transactions on Knowledge and Data Engineering.
[39] Wei Zhang,et al. Multilevel Framework to Detect and Handle Vehicle Occlusion , 2008, IEEE Transactions on Intelligent Transportation Systems.
[40] Thierry Denoeux,et al. A neural network classifier based on Dempster-Shafer theory , 2000, IEEE Trans. Syst. Man Cybern. Part A.
[41] Yiming Yang,et al. An Evaluation of Statistical Approaches to Text Categorization , 1999, Information Retrieval.
[42] Philippe Smets,et al. The Transferable Belief Model , 1991, Artif. Intell..
[43] Xiaoxia Sun,et al. Supervised Deep Sparse Coding Networks for Image Classification , 2020, IEEE Transactions on Image Processing.
[44] Weisi Lin,et al. No-Reference Image Blur Assessment Based on Discrete Orthogonal Moments , 2016, IEEE Transactions on Cybernetics.
[45] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..