A student network learns from a teacher network using self-distillation, producing emergent semantic attention maps. [Read More]

Randomly mask image patches and reconstruct the missing ones to learn context-aware visual representations. [Read More]

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]

It stabilizes and scales contrastive learning by maintaining a dynamic dictionary with momentum-based updates, becoming a cornerstone for modern SSL methods. [Read More]

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]

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]

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]

This article introduces the basic concepts and rigorous formulas of probability, serving as the foundation for understanding random variables, sampling, and MCMC. [Read More]

Understand concepts of random variables, PDF, expectation, and sampling methods for common distributions (Uniform, Normal, Exponential). [Read More]

Learn about raster and vector data and set up your Python environment [Read More]