A survey of deep network techniques all classifiers can adopt
Deep neural networks (DNNs) have introduced novel and useful tools to the machine
learning community. Other types of classifiers can potentially make use of these tools as well …
learning community. Other types of classifiers can potentially make use of these tools as well …
Coarse-to-fine: Progressive knowledge transfer-based multitask convolutional neural network for intelligent large-scale fault diagnosis
In modern industry, large-scale fault diagnosis of complex systems is emerging and
becoming increasingly important. Most deep learning-based methods perform well on small …
becoming increasingly important. Most deep learning-based methods perform well on small …
Label relation graphs enhanced hierarchical residual network for hierarchical multi-granularity classification
Hierarchical multi-granularity classification (HMC) assigns hierarchical multi-granularity
labels to each object and focuses on encoding the label hierarchy, eg,[" Albatross"," Laysan …
labels to each object and focuses on encoding the label hierarchy, eg,[" Albatross"," Laysan …
[HTML][HTML] Unimodal regularisation based on beta distribution for deep ordinal regression
Currently, the use of deep learning for solving ordinal classification problems, where
categories follow a natural order, has not received much attention. In this paper, we propose …
categories follow a natural order, has not received much attention. In this paper, we propose …
A parallel grid-search-based SVM optimization algorithm on Spark for passenger hotspot prediction
Predicting passenger hotspots helps drivers quickly pick up travelers, reduces cruise
expenses, and maximizes revenue per unit time in intelligent transportation systems. To …
expenses, and maximizes revenue per unit time in intelligent transportation systems. To …
Fabric retrieval based on multi-task learning
J Xiang, N Zhang, R Pan, W Gao - IEEE Transactions on Image …, 2020 - ieeexplore.ieee.org
Due to the potential values in many areas such as e-commerce and inventory management,
fabric image retrieval, which is a special case in Content Based Image Retrieval (CBIR), has …
fabric image retrieval, which is a special case in Content Based Image Retrieval (CBIR), has …
Hierarchical semantic risk minimization for large-scale classification
Hierarchical structures of labels usually exist in large-scale classification tasks, where labels
can be organized into a tree-shaped structure. The nodes near the root stand for coarser …
can be organized into a tree-shaped structure. The nodes near the root stand for coarser …
Tumor bagging: a novel framework for brain tumor segmentation using metaheuristic optimization algorithms
SN Shivhare, N Kumar - Multimedia Tools and Applications, 2021 - Springer
Brain tumor segmentation is a challenging research problem and several methods in
literature have been suggested for addressing the same. In this paper, we propose a novel …
literature have been suggested for addressing the same. In this paper, we propose a novel …
Integrating model-and data-driven methods for synchronous adaptive multi-band image fusion
A novel synchronous adaptive framework for multi-band image fusion is proposed, based on
integrated model-and data-driven (MDDR) techniques. This approach includes a deep stack …
integrated model-and data-driven (MDDR) techniques. This approach includes a deep stack …
Bio-inspired representation learning for visual attention prediction
Visual attention prediction (VAP) is a significant and imperative issue in the field of computer
vision. Most of the existing VAP methods are based on deep learning. However, they do not …
vision. Most of the existing VAP methods are based on deep learning. However, they do not …