Representation learning;Task analysis;Noise reduction;Purification;Denoising diffusion probabilistic model (DDPM);feature purification;feature selection;hyperspectral image (HSI) ...

Abstract: In recent years, self-supervised learning has made rapid progress in the field of hyperspectral image classification ... contrastive learning and diffusion models by training the diffusion ...

Counterfactual explanations (CEs) aim to enhance the interpretability of machine learning ... medical image properties. Method overview: The proposed method involves three steps: Unsupervised training ...

Department of Computer Science, Cornell University, Ithaca, NY, United States Out-of-distribution (OOD) detection is crucial for enhancing the reliability of machine learning models ... detection ...

The study comprehensively evaluates diffusion-based representation learning across various datasets and tasks, shedding light on their potential derived solely from images. The proposed model ...

Aim: This paper aims to test the feasibility and effectiveness of Denoising Diffusion Probabilistic ... citrus leaf images for disease classification through transfer learning. The Method 2 utilizes ...

Adobe Firefly: Image generation AI developed by Adobe. Images in Adobe Stock and public domain images are used for learning ... of generating Stable Diffusion (model: Ultimate Diffusion) looks ...

Abstract: The effectiveness of spectral-spatial feature learning is crucial for the hyperspectral image (HSI ... diffusion features for HSI classification for the first time, called MTMSD.