Explore the application of autoencoders for image colorization in this 21-minute tutorial. Learn how to trick autoencoders by training them on one set of images to reconstruct slightly different variations, enabling the artificial colorization of black and white images. Dive into the Python implementation of this technique, following a step-by-step process that covers introduction, code setup in Lab Space, Tan H testing, model execution, fitting, and printing. Conclude with the exciting process of colorizing images using the trained model. Access the complete code on GitHub to practice and expand your skills in deep learning and image processing.
Overview
Syllabus
Introduction
Code
Lab Space
Tan H
Testing
Running the model
Fitting the model
Printing the model
Colorizing the model
Taught by
DigitalSreeni
