Скачать или смотреть ProGAN From Scratch in TensorFlow | Progressive Growing GAN Explained (CodeCatalyst)

At CodeCatalyst, we focus on understanding AI systems deeply and implementing them correctly from first principles.

In this video, we implement Progressive Growing GAN (ProGAN) from scratch using TensorFlow, while building a strong intuition for why progressive growing stabilizes GAN training at high resolutions.

Rather than copying library abstractions, we:
Design the Generator and Discriminator step by step
Implement progressive resolution growth (4×4 → higher resolutions)
Handle fade-in layers, loss functions, and stabilization tricks
Write a clean, research-inspired TensorFlow training loop

This tutorial bridges the gap between reading the ProGAN paper and actually implementing it.


🧠 What You’ll Learn
Core intuition behind Progressive Growing GANs
Why standard GANs fail at high resolutions
Progressive resolution scaling (4×4 → 8×8 → 16×16 → …)
Building ProGAN Generator from scratch
Building ProGAN Discriminator from scratch
Fade-in mechanism and smooth transition between resolutions
GAN loss functions and training stability techniques
Complete TensorFlow-based ProGAN training pipeline


⏱️ Chapters
00:29 – Importing Models
13:58 – Coding Models
30:21 – Coding Training Loop
49:44 – Training Data
55:26 – Training Samples



🏷️ Hashtags

#CodeCatalyst
#ProGAN
#ProgressiveGAN
#GANFromScratch
#TensorFlowGAN
#DeepLearning
#AIResearch
#GenerativeModels
#GANTraining
#MachineLearning
#AdvancedDeepLearning
#NeuralNetworks
#ComputerVision
#TensorFlowTutorial
#AIEngineering