CycleGAN is a generative adversarial network framework designed for unpaired image-to-image translation. It enables the conversion of images between two distinct visual domains using datasets that do not require direct one-to-one matching examples.
The project implements a deep learning style transfer tool capable of artistic style transfer, object transfiguration, and domain-to-domain conversion. It uses a dual-generator architecture and cycle-consistency loss to ensure that images translated to a target domain and back recover their original state.
The framework covers core machine learning workflows including generative translation model training and training data refinement to translate synthetic datasets into realistic styles. It also includes tools for real-time training visualization to monitor image transformations during the training and testing processes.
This project is built using PyTorch.
