The effectiveness of spectral-spatial feature learning is crucial for the hyperspectral image (HSI) classification task. Diffusion models, as a new class of groundbreaking generative models, have the ...

Contrastdm: Combining Contrastive Learning and Diffusion Model for Hyperspectral Image Classification Abstract: In recent years, self-supervised learning has made rapid progress in the field of ...

Counterfactual explanations (CEs) aim to enhance the interpretability of machine learning models by illustrating how alterations in input features would affect the resulting predictions. Common CE ...

Methods: Two training methods are proposed: The Method 1 employs the DDPM to generate synthetic images for data augmentation. The Swin Transformer model is then used for pre-training on the synthetic ...

Specifically, we examine the Denoising Diffusion Probabilistic Model (DDPM) and the Latent Diffusion Model (LDM), along with other generative sampling choices. Both models demonstrated the ability to ...

SODA showcases its strengths in classification, reconstruction, and synthesis tasks, including high-performance few-shot novel view generation and semantic trait controllability. A SODA model utilizes ...

When generating images and sounds with AI, rather than simply inputting labeled data and allowing it to learn, a separate classification model (classifier) is prepared at the time of sampling ...

MTMSD: Exploring Multi-Timestep Multi-Stage Diffusion Features for Hyperspectral Image Classification This repository is the official implementation of MTMSD: Exploring Multi-Timestep Multi-Stage ...