Implementation: CycleGAN from Scratch with PyTorch + Image-to-Image Translation скачать в хорошем качестве

Implementation: CycleGAN from Scratch with PyTorch + Image-to-Image Translation

Welcome back! Today, we’re diving into the fascinating world of CycleGANs — an innovative architecture that enables unpaired image-to-image translation. In this video, you’ll learn: Why CycleGANs are a game-changer for image translation tasks when paired data isn’t available What a CycleGAN is and how it leverages cycle consistency to achieve amazing results The complete architecture breakdown — including how the generator and discriminator work together to create unpaired image translations A line-by-line PyTorch implementation from scratch inside Google Colab Training tips and visualizations to show the model's progress and how it learns to map images between domains How to convert images from one domain to another (e.g., photos to artwork, horses to zebras, etc.) using a trained CycleGAN We cover everything — from theory to architecture to coding to training to results — so you’ll not only understand CycleGANs but also know how to implement and train one for your own image translation tasks! Perfect for: Anyone familiar with GANs and interested in exploring unpaired image-to-image translation with deep learning! Topics Covered: CycleGAN, PyTorch, Image-to-Image Translation, Generative Models, Unsupervised Learning Notebook Reference: This video is based on the Kaggle notebook by lmyybh - "pytorch-cycleGAN" Link: https://www.kaggle.com/code/lmyybh/py... Reference Links: Easy to understand explaination along with implementation:   / cyclegan-introduction-pytorch-implementation   #CycleGAN #PyTorch #DeepLearning #GenerativeAI #ImageTranslation #UnsupervisedLearning #NeuralNetworks