incorporated Long-Short-Term Memory in the Siamese network architecture, which enables the extraction of long sequences of textual information and obtains global feature information, addressing the problem that RNN is unable to learn the information dependency of text over long distances. Mueller [25]...
First, the fault samples were input into the same feature extraction network in pairs, and a long and short-term memory (LSTM) network and convolutional neural network (CNN) were used to map the bearing signal data to the low-dimensional feature space. Then, the extracted sample features were...
In order to learn them, we make use of a simple architecture formed by an encoder, responsible for extracting image features, and three multilayer perceptrons that act as regressors for sˆ, qˆ and tˆ, which are the outputs of the ...
Training a Siamese Network To train a Siamese network, we need to define the architecture of the network, the loss function, and the optimization algorithm. Let’s walk through a simple example of training a Siamese network using Python and TensorFlow. Architecture ```mermaid classDiagram class ...
A diagram of the architecture can be seen in Fig. 1. Fig. 1 Architecture of a Siamese Neural Network where the input is a pair of elements Full size image There are two loss functions mainly used in these models, the Binary Cross Entropy and the Contrastive loss. The Binary Cross ...
In order to learn them, we make use of a simple architecture formed by an encoder, responsible for extracting image features, and three multilayer perceptrons that act as regressors for sˆ, qˆ and tˆ, which are the outputs of the network. Fig...
Although some methods in recent work have already used multi-scale feature fusion, we built our own multi-scale feature fusion network after analyzing the shortcomings of Siamese networks. Our proposed method built a novel network architecture, but other methods use the original FPN for multi-scale...
The SiamLT network architecture is illustrated in Figure 2. The network takes in a low-light image sequence as input and crops the original image to obtain a template image and a search image. The low-light feature enhancement module is applied to enhance the feature information of the templat...
3.2. Experiments about Network Architecture In this subsection, the experiments that were designed for the verification are described, and the experimental results are shown and analyzed. The experiments were divided into two parts: first, the analysis of the influence of patch size on the change ...
Due to the small size of the moving target shadow, features of the shadow will be lost caused by the large depth of the network. Thus, VGG16 is chosen and modified as the backbone because of its small network depth but powerful performance. The diagram of the structure of the backbone ...