In this way, the ViT network is enhanced to utilize local information and improve the clarity of feature boundary delineation, thus improving the accuracy of PolSAR image classification. Figure 1. The ...

Abstract: Unlike other deep learning (DL) models, Transformer has the ability to extract long-range dependency features from hyperspectral image (HSI) data. Masked autoencoder (MAE), which is based on ...

We show that these latent representations are helpful for medical condition classification ... image properties. Method overview: The proposed method involves three steps: Unsupervised training of a ...

This article explains how to use a PyTorch ... An autoencoder is a neural network that predicts its own input. The diagram in Figure 3 shows the architecture of the 65-32-8-32-65 autoencoder used in ...

PyTorch implementation of the model presented in "Satellite Image Time Series Classification with Pixel-Set Encoders ... We also propose to extract temporal features using a bespoke neural ...

The transformer model described in this study is implemented utilizing the PyTorch framework ... transformer architecture. In this study, a novel vision transformer model was presented to resolve the ...