Latent spaces are abstract, high-dimensional areas within neural networks where patterns and relationships are encoded, but not readily interpretable by humans. Although latent space studies are still ...

We present one of the first comprehensive evaluations of predictive information derived from retinal fundus photographs, illustrating the potential and limitations of readily accessible and low-cost ...

Abstract: Graph Neural Networks (GNNs) have emerged as a promising solution for few-shot hyperspectral image (HSI) classification. However, existing GNN-based approaches face critical limitations in ...

Abstract: Convolutional Neural Networks (CNNs) are extensively utilized for image classification due to their ability to exploit data correlations effectively. However, traditional CNNs encounter ...