Pix2Pix GAN for Image-to-Image Translation
Introduction:
Pix2Pix GAN (Generative Adversarial Network) is a deep convolutional neural network designed for image-to-image translation tasks. This approach is made possible by a carefully constructed architecture capable of generating large images up to 256x256 pixels, which performs well in various image-to-image translation tasks. This tutorial aims to show users how to create a Pix2Pix GAN for image-to-image translation.
The GAN architecture consists of a generative model that creates synthetic images and a discriminative model that classifies the images as real or fake. The generator model is updated by a discriminator model trained to distinguish the generated images from the real images. The Pix2Pix model is a conditional GAN whose output image generation depends on the input, the source image. The source and target images are passed to a classifier, and the classifier’s job is to determine if the target is a reasonable transformation of the source image. The generator is formed with the contradictory loss and the L1 loss.This will force the generator to generate realistic images similar to the expected output image in the target domain.
Related Work:
This text discusses the importance of generative adversarial networks (GANs) in image synthesis and transformation. GANs synthesize images using Gaussian or uniformly distributed noise vectors. However, the problem with this method is that the nature of the composite image cannot be determined. Therefore, GANs introduce conditional variables based on GANs that can determine image type using category labels or associated attributes. On the one hand, this method gives very good results.
On the other hand, it is also possible to artificially determine the type of composite image. To address this issue, Reed et al. I suggested image synthesis based on the text description. This method provides great flexibility in text-to-image composition because the text description contains basic object category information and can determine the specific content of the image.
The text also mentions well-known works that have benefited from GANs and various applications such as medical image processing and image-to-image conversion. The text states that there are few review articles on GAN architecture and performance. To fill this gap, the authors have collected and extensively discussed a wide range of GAN models. The text highlights various GAN models such as cVAE-GAN, cLR-GAN, BicycleGAN, progressive GAN, DTN, DualGAN, and VAE-GAN.
The authors argue that progressive GANs perform better because they can gain additional leverage through lateral connections to previously learned features. This architecture is often used to extract complex features. Furthermore, the authors emphasize using VAE-GAN networks to model each image domain and achieve cross-domain transformations through cycle-consistent and weight-dispersion constraints. Finally, the authors describe the primary rough results and stack structure to refine the results in more detail.
Image synthesis and transformation is a major research area in computer vision, with a variety of methods yielding impressive results, including: B. Autoregressive models, deterministic networks, variational autoencoders. However, Generative Adversarial Networks (GANs) have emerged as the most successful method of image generation.
Initially, GANs used Gaussian or uniformly distributed noise vectors to generate images, but this approach lacked the ability to determine the type of composite image. To overcome this limitation, GAN-based conditional variables were introduced to allow us to determine image type using category labels or related attributes. Conditional GANs can achieve good results, but they can also artificially determine the type of synthetic image. Text-to-image compositing has emerged as a very flexible approach to address this problem, as the textual description can be used to determine the specific content of the image.
METHODS
This dataset consists of satellite imagery of New York City and the corresponding Google Maps page used for the image conversion task of converting satellite imagery to Google Maps format and vice versa. The dataset, available for download as a 255-megabyte zip file from the pix2pix website, contains 1,097 images in the training folder, and a validation set is also available.
The dataset consists of satellite images of New York and corresponding Google Maps pages. A 255MB zip file containing 1097 images from the training record and 1099 images from the validation dataset can be downloaded from the Pix2Pix website. The image is in JPEG format with a digital filename and measures 1200 pixels wide by 600 pixels high. Each image contains satellite images on the left and Google Maps images on the right. Keras can be used to prepare datasets for Pix2Pix GAN model training.
Specifically, it processes images from the training dataset, loads each image, resizes it, and splits it into satellite images and Google Maps items. The resulting 1097 pairs of color images are 256 x 256 pixels wide and tall. The following function called Load Image does this by aggregating the list of images into a specific directory, loading each image with a target size of 256x512 pixels, and dividing each image into satellite and map elements. do it. You can call this function with the path of the training dataset to save the prepared array in a compressed format to a new file for later use.