Learning to Learn

Computer Vision Foundations and Model Architectures Foundations of Computer Vision and Model Architectures Computer Vision (CV) focuses on enabling machines to understand visual data . Modern CV systems rely on deep neural networks that perform tasks such as image classification, object detection, and image segmentation . This blog provides a structured overview of these tasks and the most commonly used architectures behind them. Rather than treating models as black boxes, we focus on why each architecture was introduced , what problems it solved , and where it is used today . 1. Core Vision Tasks Image Classification Assigns a single label (or multiple labels) to an entire image. $$ \hat{y} = \arg\max_y p(y \mid x) $$ Object Detection Predicts both what objects are present and where they are. $$ (\text{class}, x, y, w, h) $$ Segmentation Assigns a class label to each pixel. $$ p(y_i \mid x) $$ Classification answers what , detection ...

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. ...