Each thin blue arrow represents a neural weight, which is just a number, typically between about -2 and +2. Weights are sometimes called trainable parameters. The small red arrows are special weights ...

In today’s era of increasing data complexity and pervasive noise, robust techniques for data processing, reconstruction, and denoising are crucial. Autoencoders, known for their adaptability in ...

In today’s era of increasing data complexity and pervasive noise, robust techniques for data processing, reconstruction, and denoising are crucial. Autoencoders, known for their adaptability in ...

Each small blue arrow represents a neural weight, which is just a number, typically between about -2 and +2. Weights are sometimes called trainable parameters. The small red arrows are special weights ...

The basic scheme of the AE algorithm is shown in Figure 1. FIGURE 1. Schematic diagram of an autoencoder. As shown in Figure 1, the autoencoder has a symmetric structure consisting of two components: ...

4.2 Stacked autoencoder network The stacked autoencoder network of totally seven layers is established in the present study. The input and output parameters for each layer are listed in Table 2. The ...

Masked autoencoders (MAEs) are a self-supervised pretraining strategy for vision transformers (ViTs) that masks-out patches in an input image and then predicts the missing regions. Although the ...