Explore the world of CycleGANs in this comprehensive lecture by child prodigy Tanishq Abraham. Delve into the intricacies of this novel deep learning tool and its applications in pathology. Learn about unpaired image conversion problems, model architecture, and training techniques. Discover the potential and limitations of CycleGANs through code demonstrations and real-world examples in pathology and microscopy. Gain insights into generator and discriminator architectures, loss functions, and implementation using popular deep learning frameworks. Explore practical aspects such as learning rate scheduling, inference, and creating web interfaces for CycleGAN models. Understand the challenges and failures associated with CycleGANs, particularly in the context of pathology applications.
What Are CycleGANs? - A Novel Deep Learning Tool in Pathology
Overview
Syllabus
Introduction
Objectives
Colorization
Super Resolution
Unpaired Image Translation
Examples of Unpaired Problems
CycleGANs
Review
Generator and Discriminator
Generator Architecture
Discriminator Architecture
Loss Function
Code
Package
MBdev
Model Architecture
Tests
Discriminator
Identity Loss
Documentation
Transforms
Data Loader
FastAI
CycleGAN Trainer
Learning Rate Scheduling
Adding Learning Rate Scheduling to the learner object
Creating the learner class
Learning rate finder
Performing inference
Example code
Examples
Web Interface
Prediction Function
Interface
Cropping
CycleGAN failure
CycleGANs in pathology
Taught by
Abhishek Thakur
