SwAV: Swapping Assignments between Views
Simultaneously cluster the data and learn visual representations by enforcing consistency between cluster assignments, or ‘codes’, generated from different augmented views of the same image.
[Read More]
Series & Collections
🤖 AI Fundamentals
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:
Latest Articles
MoCo: Momentum Contrast for Unsupervised Visual Representation Learning
It stabilizes and scales contrastive learning by maintaining a dynamic dictionary with momentum-based updates, becoming a cornerstone for modern SSL methods.
[Read More]
Mastering TerraMind: From Understanding to Fine-tuning
TerraMind is the first large-scale, any-to-any generative multimodal foundation model proposed for the Earth Observation (EO) field. It is pre-trained by combining token-level and pixel-level dual-scale representations to learn high-level contextual information and fine-grained spatial details. The model aims to facilitate multimodal data integration, provide powerful generative capabilities, and support zero-shot and few-shot applications, while outperforming existing models on Earth Observation benchmarks and further improving performance by introducing ‘Thinking in Modalities’ (TiM).
[Read More]
Monte Carlo Sampling
Understand the core concepts of Monte Carlo: Law of Large Numbers, rejection sampling, importance sampling, variance reduction techniques (antithetic variates, control variates, stratified sampling).
[Read More]
Introduction to MCMC
The reason we need MCMC is that many distributions are only known in their unnormalized form, making traditional sampling/integration methods ineffective. By constructing a ‘correct Markov chain’, we can obtain the target distribution from its stationary distribution, meaning the long-term distribution of the trajectory ≈ target distribution.
[Read More]
What is Probability?
This article introduces the basic concepts and rigorous formulas of probability, serving as the foundation for understanding random variables, sampling, and MCMC.
[Read More]
Random Variables and Sampling
Understand concepts of random variables, PDF, expectation, and sampling methods for common distributions (Uniform, Normal, Exponential).
[Read More]
Lesson 1: Introduction to Remote Sensing Data and Python Setup
Learn about raster and vector data and set up your Python environment
[Read More]
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]