The diagram in Figure 2 illustrates a neural autoencoder. The autoencoder has the same number of inputs and outputs (9) as the demo program, but for simplicity the illustrated autoencoder has ...

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 ...

That said, applying a neural autoencoder anomaly detection system to tabular data is typically the best way to start. A limitation of the autoencoder architecture presented in this article is that it ...

We present a novel granular computing approach that assesses landslide risk by combining fuzzy information granulation and a stacked autoencoder algorithm. The stacked autoencoder is trained using an ...

2.1 Stacked autoencoder network The structure of stacked autoencoder network is relatively simple, where autoencoder is employed as the basic neural of deep learning network. A typical autoencoder ...

Although the approach is both simple and effective, the MAE pretraining objective is currently restricted to a single modality — RGB images — limiting application and performance in real-life ...