CycleGAN

What is CycleGAN?

CycleGAN is a generative adversarial network (GAN) architecture designed for unsupervised image-to-image translation tasks. The main idea behind CycleGAN is to learn a mapping between two different image domains without requiring paired examples, unlike traditional supervised image-to-image translation methods. CycleGAN utilizes a cycle-consistency loss, which ensures that an image translated from one domain to the other and back again should resemble the original image. This constraint allows the model to learn meaningful translations between the two domains even in the absence of explicit paired examples.

CycleGAN example

Here’s a simple example of how to use a pre-trained CycleGAN model for image-to-image translation using the PyTorch library:

import torch
from torchvision import transforms
from PIL import Image
from models import Generator

# Load the pre-trained CycleGAN generator model
generator = Generator()
generator.load_state_dict(torch.load("path/to/pretrained_model.pth"))
generator.eval()

# Load and preprocess the input image
input_image = Image.open("path/to/input_image.jpg")
preprocess = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
input_tensor = preprocess(input_image).unsqueeze(0)

# Perform image-to-image translation using the CycleGAN model
with torch.no_grad():
    output_tensor = generator(input_tensor)

# Convert the output tensor to an image and save it
output_image = transforms.ToPILImage()(output_tensor.squeeze())
output_image.save("path/to/output_image.jpg")

Resources for learning more about CycleGAN