Learning to Learn

Conditional Diffusion for Brightfield to Fluorescence Translation Conditional Diffusion for Brightfield to Fluorescence Image Translation Fluorescence microscopy provides critical biological insights, but acquiring fluorescent images is often time-consuming, expensive, and phototoxic . This blog describes a conditional diffusion model that translates Brightfield (BF) images into corresponding fluorescence channels (red or green), using a unified and probabilistic generative framework. Instead of predicting fluorescence directly, the model learns how to iteratively denoise fluorescence images conditioned on Brightfield structure and channel identity. Brightfield (BF) Structural cell morphology captured without fluorescence labeling. Green Fluorescence Complementary fluorescence channel with distinct biological specificity. ...

LeJEPA: Predictive World Models Through Latent Space Prediction LeJEPA: Predictive Learning With Isotropic Latent Spaces Self-supervised learning methods such as MAE, SimCLR, BYOL, DINO, and iBOT all attempt to learn useful representations by predicting missing information. Most of them reconstruct pixels or perform contrastive matching, which forces models to learn low-level details that are irrelevant for semantic understanding. LeJEPA approaches representation learning differently: Instead of reconstructing pixels, the model predicts latent representations of the input, and those representations are regularized to live in a well-conditioned, isotropic space. These animations demonstrate LeJEPA’s ability to predict future latent representations for different types of motion. The first animation shows a dog moving through a scene, highlighting semantic dynamics and object consisten...