Exploring the Potential of Image Generation Using Generative Adversarial Networks

The development of Generative Adversarial Networks (GAN) has resulted in impressive image generation algorithms. This article will explore state-of-the-art approaches to image synthesis using GANs, including both un-conditional generation and conditional generation.

Direct methods use only one generator and discriminator, while hierarchical and iterative methods utilize multiple GANs with different roles. For example, PLDT [35] performs domain transfer by modifying the geometric shape of images in two different domains simultaneously.

1. Generative Adversarial Networks (GANs)

GANs have become the de facto standard for image generation. They consist of two models — the generator and discriminator — which are in a dynamic feedback loop, with the generator algorithm trying to create data that is indistinguishable from real data and the discriminator algorithm trying to distinguish between real and generated data.

The discriminator is typically a deep neural network that aims to minimize the binary cross-entropy loss between its predictions and actual labeled images. This approach can be applied to many tasks, such as missing data imputation, text-to-image translation and even adversarial example synthesis (Sajeeda and Hossain, 2022).

A variety of approaches have been developed to mitigate the stability problems associated with GAN training. CycleGANs use ensemble learning, while Unrealistic Features Suppression (UFS) modules help to eliminate outlier nodes in the training distribution that would otherwise cause mode collapse. Alternatively, selective pressure in the form of proximal training has been shown to be effective.

2. Hierarchical Methods

Hierarchical methods model image-level correlations by linking objects together in a tree-like structure. When used in image classification and detection, they allow for greater accuracy by leveraging class-specific features. However, most existing hierarchical approaches model only visual characteristics and do not consider class information, so they are limited to visual classification and detection.

Aiming at modeling more complex correlations, this paper extends the concept of a hierarchical method to multi-layer architectures and introduces new linkage functions for better separation. It also improves the performance of these models by performing split-and-merge operations on the host layer to reduce redundant features and increase dimensionality.

The resulting models have better quality and more diversity compared to the baselines. Moreover, the distribution of synthetic images by HA-GAN is closer to the real data distribution compared with the other models. This result indicates that the hierarchical model can incorporate a richer set of attributes into the generated images and improve their quality.

3. Iterative Methods

Using iterative methods, we can learn a solution to a problem by repetitively testing different values of an initial estimate. This approach is a common technique in many disciplines, including mathematics, physics and computer science.

Shen et al [88] uses an iterative algorithm to solve a system of linear equations, generating a new image G(x) each time it iterates by adding an attribute to the manipulated input x. They also use a discriminator similar to the ternary classifier in DTN to distinguish between ground-truth and manipulated images.

Another iterative method is score matching, which models the gradient of data log-density with respect to the image. This model is often used in denoising and has been extended to natural image generation, producing high quality unconditional images, as well as high resolving power on a wide variety of tasks such as face super-resolution. SR3 (Stochastic Iterative Diffusion Probabilistic Model) [29] is one such model, showing good performance on both faces and natural images, as well as high resolution for super-resolution.

4. Text-to-Image Synthesis

Image synthesis is an important machine learning technology that allows us to generate images using a natural language description. It can be used in a variety of applications including image editing, graphic design and more. This year, the output of state-of-the-art text-to-image models have started to approach artistic and photorealistic levels.

These methods use a GAN model that is trained to map text features to image pixels. A second model is then conditioned on the first GAN and generates high-resolution photo-realistic images.

The key component of this architecture is the attentive generator and discriminator, which are designed to enable text-to-image synthesis based on semantic layout. It uses attention-driven context vectors to encode words that are relevant to a particular bounding box and an alignment module to ensure the generated image is semantically consistent with the input text description. StackGAN achieves new records on the CUB and Oxford-102 datasets in terms of Inception score and visual-semantic similarity.