This paper presents a deep learning network that performs automatic detection of defects by inspecting full ultrasonic guided wave signals excited in plate structures. The findings show that the algorithm, which is an adaptation of WaveNet, and hence is based on causal dilated convolutional neural ...
The Self-Attention Causal Dilated Convolutional Neural Network (SACDCNN) is proposed to address the limitations of existing models that perform poorly on classification tasks. It designs the residual and dense blocks based on Causal Dilated Convolution based on the traditional residual and dense ...
Deep Pyramid Convolutional Neural Network Integrated with Self-attention Mechanism and Highway Network for Text Classification Text classification is one of the basic tasks of natural language processing. In recent years, deep learning has been widely used in text classification tasks. The representative...
As a regression model, we use a small LeNet-like convolutional neural network49; the code provides more details. As a loss function, we use the mean-squared error in predicting the light-source settings R, G, B, which we minimize using the stochastic gradient descent. We fit the model ...
& Roy, K. Tree-CNN: a hierarchical deep convolutional neural network for incremental learning. Neural Netw. 121, 148–160 (2020). Article Google Scholar Kim, J., Kim, B., Roy, P. P. & Jeong, D. Efficient facial expression recognition algorithm based on hierarchical deep neural network...
These elements are rendered into bird’s-eye view images using different colors or masks to allow convolutional neural networks to capture the scene context. However, the rasterization process inevitably leads to the loss of geometric and temporal details. High-definition (HD) maps serve as a ...
tends to make deep learning models more effective. The most typical deep learning models include convolutional neural networks (CNNs), recurrent neural networks (RNNs), and variants of the latter like long short-term memory (LSTM) and gated recurrent units (GRU). Later, the introduction of uns...
Improved regular- ization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552, 2017. 7 [16] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Syl- vain Gelly, ...
Causal convolutional network Due to the great success in the field of image recognition, the convolutional neural network (CNN) has become more and more popular in recent years [33], [34]. Unlike the fully connected neural network, CNN uses a local link structure to share common weights in ...
(SRDCC-BiLSTM) focuses on the mitigation of the over-fitting problem and avoids the overwhelming of model parameters, such as the size of the convolutional layer that includes weights of the filter, bias unit, and dimensions of the input series in Dilated Causal Convolution Neural Network (...