Table 13 Sensitivity of the D-Path-AE to Noise: The table reports the classification accuracy across different datasets when Gaussian noise is added to the input features during training. Testing is performed on clean data. The results demonstrate the model’s ability to learn robust and meaningful features, effectively handling noisy training data without compromising accuracy.

From: Enhancing feature learning of hyperspectral imaging using shallow autoencoder by adding parallel paths encoding