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A Survey on Generative Adversarial Networks: Variants, Applications, and Training
Abdul Jabbar, Xi Li, Bourahla Omar
TL;DR
GAN research has achieved realistic generation and broad applications, but training remains unstable because of optimization, diversity, gradient, and evaluation challenges. This survey synthesizes GAN variants, applications, obstacles, and proposed stabilization techniques, concluding that further work is needed for stable convergence and evaluation.
Problem
GAN training remains unstable because of Nash-equilibrium difficulty, internal covariate shift, mode collapse, vanishing gradients, and a lack of standard evaluation metrics.
Method
The paper surveys classical GAN variants, applications across computer vision and other domains, training obstacles, and proposed solutions for stabilizing GAN training.
Results
The survey identifies multiple training techniques that researchers have proposed to stabilize GANs and address previous limitations.
Takeaways & Limitations
GANs support practical applications in image, audio, and video domains, while stable training remains central to their successful use.
Takeaways & Limitations
Further solutions are still needed to make GAN training more stable and help models converge to Nash equilibrium, while model evaluation remains unresolved.
Abstract
from arXiv · showhide
The Generative Models have gained considerable attention in the field of unsupervised learning via a new and practical framework called Generative Adversarial Networks (GAN) due to its outstanding data generation capability. Many models of GAN have proposed, and several practical applications emerged in various domains of computer vision and machine learning. Despite GAN's excellent success, there are still obstacles to stable training. The problems are due to Nash-equilibrium, internal covariate shift, mode collapse, vanishing gradient, and lack of proper evaluation metrics. Therefore, stable training is a crucial issue in different applications for the success of GAN. Herein, we survey several training solutions proposed by different researchers to stabilize GAN training. We survey, (I) the original GAN model and its modified classical versions, (II) detail analysis of various GAN applications in different domains, (III) detail study about the various GAN training obstacles as well as training solutions. Finally, we discuss several new issues as well as research outlines to the topic.
1 Introduction
GANs provide a generative framework for unsupervised and semi-supervised learning, enabling diverse applications across computer vision and machine learning. This survey reviews GAN variants, applications, training obstacles, stabilization techniques, and future directions.
- Generative Adversarial Networks: GANs emerged as generative models for unsupervised learning and became common in both unsupervised and semi-supervised learning.The framework was introduced by Goodfellow et al. in 2014.
- Applications: GANs have been applied to image, audio, and video tasks including synthesis, translation, detection, editing, super-resolution, and speech or music generation.Examples include face aging, image in-painting, object detection, image-to-image translation, video generation, and audio synthesis.
- Training obstacles: The survey identifies unstable training as a central challenge caused by issues including Nash equilibrium, internal covariate shift, mode collapse, vanishing gradients, and inadequate evaluation metrics.These obstacles complicate alternating or simultaneous optimization of the generator and discriminator.
- Survey scope: The survey reviews classical GAN models and variants, comparative analyses of their merits and demerits, and related literature surveys.Tables summarize reviewed variants and related survey papers.
- Training solutions: It surveys stabilization approaches including feature matching, mini-batch discrimination, historical averaging, two time-scale updates, normalization, alternative losses, gradient penalties, and cycle consistency.These methods are presented as proposed solutions to GAN training instability.
- Future directions: The paper concludes by discussing unresolved issues and potential future research directions for improving GAN training and applications.The survey emphasizes training stability as its core focus while also considering broader practical domains.
2 Background
Generative models learn distributions from training data to produce new samples, while GAN variants extend this framework for controllability, image quality, diversity, and training stability. The surveyed variants address limitations through architectural changes, alternative objectives, regularization, and progressive training.
- Generative models: Generative models learn from an unknown data-generating distribution to produce new samples resembling the training data.They also support realistic-sample generation, missing-data handling, latent-representation inference, and unsupervised density estimation.
- Classical variants: InfoGAN adds mutual-information regularization to disentangle discrete and continuous latent factors and make generated images more controllable and interpretable.The framework is unsupervised and is described as requiring no more training time than standard GAN.
- Training-stability variants: WGAN replaces binary discrimination with a Wasserstein-distance objective, avoiding vanishing gradients and partially reducing mode collapse.WGAN-GP extends this approach with a gradient penalty enforcing the discriminator’s 1-Lipschitz condition and is reported to improve stability and convergence.
- Architectural variants: BEGAN uses an autoencoder discriminator and Wasserstein reconstruction loss to manage the trade-off between sample variety and quality.PGGAN instead grows generator and discriminator depth synchronously from low to high resolution, while BigGAN reports an inception score of at least 166 versus earlier work around 50.
- Controllability: StyleGAN redesigns the generator so scale-specific styles control coarse, medium, and fine image features without compromising generated-image quality.Its mapping network and adaptive instance normalization transform latent codes into layer-wise styles.
3 Applications
GANs support diverse applications across image, audio, and video generation, reconstruction, translation, detection, and synthesis. The surveyed systems adapt GAN architectures with conditioning, multiple stages, specialized subnetworks, or latent representations for domain-specific goals.
- Applications: GANs are applied across image, audio, and video domains, including generation, manipulation, translation, detection, and synthesis.Examples include image in-painting, text-to-image synthesis, pose transfer, steganography, texture synthesis, and video generation.
- Applications: PS-CGAN generates high-resolution 1024 X 1024 full-body anime characters with specified pose sequences.
- Applications: PGGAN improves image in-painting by combining global and patch-wise GANs to aggregate local and global information.
- Applications: AttnGAN progressively generates text-conditioned images from low to high resolution using region-level word conditioning and multimodal similarity.StackGAN instead uses consecutive stages, with Stage-I producing a low-resolution image and Stage-II enhancing its resolution.
- Applications: Specialized GAN designs address pose transfer, occluded-object detection, texture synthesis, and multimodal image-to-image translation.SeGAN combines segmentation and generation, PSGAN synthesizes diverse high-resolution textures, and MUNIT and DRIT generate varied translation outputs.
4 GAN training
This section surveys GAN training obstacles and techniques intended to improve training for more realistic data generation.
- The survey reviews GAN training obstacles and techniques intended to improve training for generating more realistic data.
4.1 Problems with training GAN
GAN training is difficult because competing networks must reach equilibrium while avoiding instability, collapsed diversity, weak generator gradients, and inconsistent evaluation.
- Nash equilibrium: GAN training seeks a Nash equilibrium between generator and discriminator, but independently updated objectives make that equilibrium difficult to achieve.The paper illustrates equilibrium as a saddle point in a two-player optimization problem.
- Nash equilibrium: Increasing iterations can amplify oscillations and training instability.
- Internal covariate shift: Internal covariate shift changes activation input distributions after earlier-layer updates, slowing learning and requiring longer training.
- Mode collapse: Mode collapse causes the generator to produce identical or narrowly varied outputs, limiting coverage of real-data modes.In the illustrated process, the generator switches modes as the discriminator rejects its currently selected mode.
- Vanishing gradient: Vanishing gradients can prevent earlier generator layers from learning when a well-trained discriminator confidently rejects generated samples.
- Evaluation: GAN methods lack a standard evaluation function, leaving no consensus parameters for fair comparison.
4.2 Analysis of GAN training obstacles
The survey links major GAN training obstacles to specific mechanisms and reviews remedies proposed to improve equilibrium, normalization, diversity, and gradient behavior.
- Nash equilibrium: WGAN, TTUR, and LSGAN are presented as methods addressing the difficulty of reaching Nash equilibrium when generator and discriminator updates lack coordination.
- Internal covariate shift: Internal covariate shift is attributed to continuously changing network parameters and is targeted by normalization techniques such as Batch Normalization.
- Mode collapse: WGAN, MBD, UGAN, DRAGAN, AdaGAN, MRGAN, and MAD-GAN are described as remedies that discourage optimizing against a single rigid discriminator and expand generator diversity.
- Vanishing gradient: WGAN, LSGAN, LS-GAN, RGAN, SN-GAN, and Batch Normalization are designed to avert vanishing gradients associated with the standard discriminator sigmoid objective and powerful discriminators.
4.3 Techniques to improve GAN training
The survey reviews techniques designed to stabilize GAN training and reduce mode collapse. These methods modify generator objectives, discriminator updates, or how examples are processed during training.
- Unstable training is a major GAN problem, motivating solutions for more successful generative-model implementation.The survey identifies Nash equilibrium, internal covariate shift, mode collapse, vanishing gradients, and inadequate evaluation metrics as contributing shortcomings.
- Feature matching changes the generator cost function so generated data reflects real-data statistics rather than explicitly maximizing the discriminator output.The technique is intended to prevent overfitting to the current discriminator.
- 4.3.2 Unrolled GAN (UGAN): Unrolled GAN updates the generator by unrolling discriminator optimization steps, which can noticeably reduce mode dropping.When the generator tends toward one mode, discriminator responses to generator changes raise the generator loss, putting off mode collapse.
- Mini-batch discrimination: Mini-batch discrimination processes multiple examples jointly, using feature-derived distances and aggregated outputs to help avoid generator mode collapse.Its procedure forms a matrix from feature vectors and a tensor, computes pairwise L1 distances, applies a negative exponential, and aggregates the results.
4.3.4 Historical averaging (HA)
Historical averaging stabilizes GAN optimization by incorporating past parameter values into both networks’ cost functions, while TTUR uses unequal learning rates to approach equilibrium. The section also situates these methods among hybrid, attention-based, and interpolation variants.
- 4.3.4 Historical averaging (HA): Historical averaging adds an average of past parameters to the cost functions of both the generator and discriminator.The past parameter value at time i is represented as θ[i].
- Two time-scales rule (TTUR): TTUR assigns the generator a slower learning rate and the discriminator a faster learning rate to reach Nash equilibrium.The discriminator learning rate is described as four times greater than the generator learning rate.
- Hybrid modeling: VAE-GAN combines a VAE decoder with a GAN generator and supplements reconstruction loss with a learned feature-space similarity metric.The architecture is reported to produce sharp, realistically looking samples.
- Self-Attention GAN: Self-Attention GAN uses broader feature-space information across image regions to coordinate distant details in multi-class image generation.The attention mechanism is described as measuring an attention map from two transformed feature spaces, with matrix multiplication denoted by ⊗.
4.3.8 Relativistic GAN (RGAN)
The section surveys relativistic discrimination, softened labels, spherical interpolation, optimization algorithms, and normalization methods as approaches to improve GAN training behavior. These techniques differ in whether they alter discriminator comparisons, sampling, optimization, or network statistics.
- 4.3.8 Relativistic GAN (RGAN): Relativistic GAN trains the discriminator to judge whether one image is more real than another, rather than classify images independently as real or fake.Its discriminator is described as pushing toward .5 instead of 1, with more stable training than standard GAN.
- Label smoothing: Label smoothing replaces hard labels with softened values such as 0.9 for real samples and 0.1 for fake samples to improve discriminator training.The survey highlights its usefulness for high-quality networks trained on relatively modest-sized datasets.
- Sampling GAN (S-GAN): Sampling GAN uses spherical linear interpolation instead of linear interpolation, and the survey reports better experimental performance for the spherical method.The spherical interpolation series is described as crisper and more visually consistent with endpoint styles than the blurred linear series.
- Optimization algorithms: Among listed optimization algorithms, ADAM is described as the most popular and as improving convergence speed and GAN training stability.The survey also lists RMSProp, SGD, and AdaGrad as optimization choices that can improve stability.
- Normalization: Batch normalization reduces internal covariate shift by normalizing each mini-batch using its mean and variance, helping deeper models avoid mode collapse.Other surveyed normalization methods include weight, instance, group, batch-instance, virtual batch, layer, and spectral normalization.
- Normalization: Spectral normalization stabilizes training by normalizing discriminator weights and ensuring the discriminator is Lipschitz continuous.The survey states that applying spectral normalization in the generator can also limit parameter-magnitude acceleration and unusual gradients.
4.3.13 Add instance noise
The cited figure presents instance noise as improving vanilla GAN convergence power and training stability.
- GANs with instance noise show better convergence power and training stability than vanilla GANs.
4.3.14 Train with labels
Conditional labeling adds information to the latent space and enables control over generated samples, unlike vanilla GANs.
- 4.3.14 Train with labels: Adding labels to the latent space improves GAN training by conditioning both the generator and discriminator.Conditional variables control sample generation through additional information supplied to the model.
4.3.15 Alternative cost functions
Alternative objectives address GAN training problems by changing the loss function or regularizing discriminator behavior. The surveyed methods target vanishing gradients, mode collapse, stability, and convergence.
- 4.3.15 Alternative cost functions: LSGAN replaces sigmoid cross-entropy with least-squares loss to mitigate vanishing gradients and bring generated samples closer to real data.The loss both classifies real and fake samples and reduces their distance for improved generation.
- 4.3.15 Alternative cost functions: WGAN-GP replaces weight clipping with a gradient penalty that improves training stability and reduces mode collapse.The penalty encourages smoother discriminator decision boundaries and is associated with better convergence, training speed, and sample quality.
- 4.3.15 Alternative cost functions: CycleGAN minimizes reconstruction distance after translating images from one domain to another and back.Its discriminators assess whether translated inputs belong to the corresponding domains.
4.4 Performance analysis
Feature matching improved diversity in text-to-image synthesis, while mode collapse produced low-diversity outputs in the comparison shown in Figure 17.
- 4.4 Performance analysis: Feature matching enabled StackGAN text-to-image synthesis to produce higher output diversity than previous standard GAN-based approaches.The comparison is illustrated in Figure 17, where the lower results show higher diversity than the mode-collapsed upper results.
5 Conclusion and future research directions
The survey reviews GAN variants, applications, training obstacles, and stabilization techniques, concluding that GANs support practical generation while retaining unresolved research challenges.
- 5 Conclusion and future research directions: The survey covers GAN models, modified classical versions, computer-vision applications, training obstacles, and proposed stabilization techniques.It frames unstable training as a central limitation despite GANs’ success in generating realistic data.
- 5 Conclusion and future research directions: Discrete data remains challenging because GANs cannot directly generate words, characters, or bytes, limiting some NLP applications.The survey lists continuous-generator and alternative-objective approaches for modeling discrete data.
- 5 Conclusion and future research directions: GAN training remains unstable because the generator and discriminator still struggle to find a saddle point corresponding to Nash equilibrium.The survey identifies WGAN, WGAN-GP, feature matching, TTUR, and spectral normalization among attempted remedies.
- 5 Conclusion and future research directions: Evaluating which GAN method is better remains difficult despite widely used metrics including IS, MS, KID, FID, MS-SSIM, and C2ST.The paper presents model evaluation as an unresolved future research direction.
- 5 Conclusion and future research directions: Mode collapse produces samples with similar properties or minimal diversity, motivating methods designed to increase diversity and visual quality.The survey states that further development of training algorithms for this scenario is still needed.
- 5 Conclusion and future research directions: The paper identifies disease prediction, Nash equilibrium, and vanishing gradients as additional GAN research problems.These topics are listed among areas requiring further attention.