A Systematic Literature Review on the Applications, Models, Limitations, and Future Directions of Generative Adversarial Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Generative adversarial networks (GANs), a subset of deep learning, have demonstrated breakthrough performance in domains such as computer vision (CV) and natural language processing (NLP), particularly in surveillance, autonomous driving, and automated programing assistance. Based on game theory principles, GANs utilize a generator–discriminator architecture to produce high‐quality synthetic data. This study conducts a systematic literature review (SLR) to comprehensively assess the development, applications, limitations, and security‐related advancements of GANs. It examines foundational models and key architectural variants, providing a critical evaluation of their roles in NLP and CV. This research explores the integration of GANs into the domain of security, highlighting their applications in information security, cybersecurity, and artificial intelligence (AI)‐driven defense mechanisms. The study also discusses prominent evaluation metrics such as inception score (IS), Fréchet inception distance (FID), structural similarity index measure (SSIM), and peak signal‐to‐noise ratio (PSNR) to assess GAN performance. Key strengths of GANs, including their ability to generate high‐resolution data and support domain adaptation, are emphasized as driving factors for their continued evolution and adoption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it