A dyadic translation is a shift by the amount n/2m that is an integer multiple of the binary scale factor and of the width of the wavelet as well. The discrete WT (DWT) is the natural choice for digital impleme
4.2 Intelligent diagnosis through attention based bidirectional recurrent neural networks Currently, as the use of deep learning is relatively new in the field of radiogenomics analysis, a large number of manually labeled data is used. This is resource intensive and impractical. Many current cancer im...
At present, deep neural network (DNN) technology is often used in intelligent diagnosis research. However, the huge amount of calculation of DNN makes it difficult to apply in industrial practice. In this paper, an advanced multiscale dense connection deep network MSDC-NET is designed. A well-...
For instance, WPT needs to choose the suitable wavelet kernel function [8] and VMD need to set the penalty factor α and the number of intrinsic mode functions (IMFs) K before processing the vibration signals [10], thereby the self-adaptive capacity of them is poor. EMD enjoys good ...
the tourism demand forecasting literature has paid increasing attention to the potential of Artificial Intelligence and Machine Learning models to capture and model the nonlinear data features in tourist arrivals with an assumption-free and data-driven approach [20]; typical models include Neural Networks...
A wavelet decomposition, therefore, leads to multiscale representations with regards to spatial ratio, frequency range and orientation for a medical image. In the next sub section, here we utilize wavelet multiscale presenta- tions as regressing features to train numerous convolutional neural networks ...
Hierarchical Multi-Attention Transfer for Knowledge Distillation. ACM Trans. Multim. Comput. Commun. Appl. 2024, 20, 51:1–51:20. [Google Scholar] [CrossRef] Chen, X.; Su, J.; Zhang, J. A Two-Teacher Framework for Knowledge Distillation. In Proceedings of the Advances in Neural Networks...
Hierarchical Multi-Attention Transfer for Knowledge Distillation. ACM Trans. Multim. Comput. Commun. Appl. 2024, 20, 51:1–51:20. [Google Scholar] [CrossRef] Chen, X.; Su, J.; Zhang, J. A Two-Teacher Framework for Knowledge Distillation. In Proceedings of the Advances in Neural Networks...
This module enables the input feature maps to be refined in channel and spatial dimensions, and it can be embedded into any prior convolutional neural network to enhance the feature representation capability. Figure 7. Hybrid attention module structure. Assuming an input feature map F with ...