This project is based on predicting the accuracy of the testing data set over the training data set using the MSTAR(Moving and Stationary Target Acquisition and Recognition) database and plotting the ...

python==3.10.13 torch==1.13.0 numpy==1.23.4 scipy==1.9.3 Device Requirements: The training and testing of AnyGraph ... node_classification/ ## test code for node classification │ │ ├── data_handler.py ...

However, current models still suffer from two main drawbacks: 1) they require enormous volumes of training data to avoid model overfitting and 2) there is a sharp decrease in performance when the data ...

Abstract: Graph neural networks (GNNs) have achieved impressive performance when testing and training graph data come from identical distribution. However, existing GNNs lack out-of-distribution ...

And What Does It Represent For AI Training? Synthetic data falls into two classifications: non-AI data simulation, or test-data creation using test-data management tools, and AI-generated ...