This serie provides a foundation in AI theory and methods for Earth Observation (EO) applications and research. It covers core topics such as machine learning, deep learning, self-supervised learning, generative models, optimization, and interpretability.
📂 Sub-series:
title: “BYOL Explained: Self-Supervised Learning without Negative Pairs” date: 2025-10-08 summary: “Understanding BYOL: How interactions between Online and Target networks achieve SOTA performance without negative samples. A deep dive into the architecture and loss function.” series: [“Self-Supervised Learning”] tags: [“BYOL”, “Contrastive Learning”, “SSL”, “CV”, “Paper Notes”]
[Read More]title: “SimCLR Explained: Contrastive Learning Design & Code” date: 2025-10-07 summary: “A detailed visual guide to SimCLR. Understand the logic behind stochastic data augmentation, the NT-Xent loss, and why contrastive learning works.” series: [“Self-Supervised Learning”] tags: [“SimCLR”, “Contrastive Learning”, “SSL”, “CV”, “Paper Notes”]
[Read More]