Playing with Vision Embeddings
Summary
The article explains how vision embeddings like DINOv3 map images to a 384-dimensional space, and how to invert these embeddings to generate images. It covers the use of sparse autoencoders for interpretability, feature visualization, interpolation between features, and practical demonstrations of combining features and decomposing embeddings.