arXiv AI June 26, 2026 By Abrar Alotaibi, Moataz Ahmed

Neural Architecture Search for Generative Adversarial Networks: A Comprehensive Review and Critical Analysis

arXiv:2606. 26169v1 Announce Type: cross Abstract: Neural Architecture Search (NAS) has emerged as a pivotal technique in optimizing the design of Generative Adversarial Networks (GANs), automating the search for effective architectures while addressing the challenges inherent in manual design.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Computer Vision

Sep 1

NepScript Genesis: Neural Architecture Search for Handwritten Devanagari Digit Synthesis

arXiv:2608.29540v1 Announce Type: new Abstract: This paper introduces NepScript Genesis, a Neural Architecture Search (NAS) framework for automated Generative Adversarial Network (GAN) discovery, app...

By Mausam Gurung, Prabin Neupane, Sajjan Acharya

diffusion benchmarks safety

arXiv AI

Jul 3

Population-Based Multi-Objective Training of Discriminators for Semi-Supervised GANs

arXiv:2607. 01907v1 Announce Type: cross Abstract: Semi-supervised generative adversarial networks (SSL-GANs) can exploit large unlabeled datasets while retaining a classifier in the discriminator, but their training is often unstable.

By Francisco Sede\~no, Francisco Chicano, Jamal Toutouh

diffusion benchmarks safety

arXiv AI

Jun 8

Scalable GANs with Transformers

arXiv:2509. 24935v3 Announce Type: replace-cross Abstract: Scalability has driven recent advances in generative modeling, yet its principles remain underexplored for adversarial learning.

By Sangeek Hyun, MinKyu Lee, Jae-Pil Heo

llms diffusion benchmarks safety

arXiv Machine Learning

Jun 30

A Conditional GAN for Tabular Data Generation with Probabilistic Sampling of Latent Subspaces

arXiv:2508. 00472v2 Announce Type: replace Abstract: The tabular form constitutes the standard way of representing data in relational database systems and spreadsheets.

By Leonidas Akritidis, Panayiotis Bozanis

diffusion safety

arXiv Machine Learning

Aug 12

Deciding When to Switch: E-Processes for Adaptive Minimax Training for Generative Adversarial Nets

arXiv:2608. 10096v1 Announce Type: cross Abstract: Modern data science increasingly gives rise to hypothesis-testing problems that are not naturally formulated in terms of parameters within prespecified statistical models.

By Hyunjoo Kim, Sicheng Wu, Agastya Venkatraman, Guang Lin, Sehwan Kim

diffusion multimodal benchmarks safety

arXiv AI

Jul 7

PotatoGANs: Utilizing Generative Adversarial Networks, Instance Segmentation, and Explainable AI for Enhanced Potato Disease Identification and Classification

arXiv:2405. 07332v2 Announce Type: cross Abstract: Numerous applications have resulted from the automation of agricultural disease segmentation using deep learning techniques.

By Fatema Tuj Johora Faria, Mukaffi Bin Moin, Mohammad Shafiul Alam, Ahmed Al Wase, Md. Rabius Sani, Khan Md Hasib

diffusion computer-vision safety

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NepScript Genesis: Neural Architecture Search for Handwritten Devanagari Digit Synthesis

Population-Based Multi-Objective Training of Discriminators for Semi-Supervised GANs

Scalable GANs with Transformers

A Conditional GAN for Tabular Data Generation with Probabilistic Sampling of Latent Subspaces

Deciding When to Switch: E-Processes for Adaptive Minimax Training for Generative Adversarial Nets

PotatoGANs: Utilizing Generative Adversarial Networks, Instance Segmentation, and Explainable AI for Enhanced Potato Disease Identification and Classification