EmbeddGAN introduces a new GAN framework that replaces the traditional discriminator with an embedding network trained to maximize statistical dependence between embeddings and real/fake labels using Gini distance correlation (gCor). The generator simultaneously minimizes this dependence, encouraging real and generated samples to become indistinguishable in the learned low‑dimensional embedding space. Experiments on MNIST, CIFAR‑10, and CelebA show competitive performance and notably more stable training dynamics compared to established baselines.
By MaTais Caldwell, Yixin Chen, Xin Dang, Charles Walter
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.
By Abrar Alotaibi, Moataz Ahmed
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
arXiv:2508. 01725v5 Announce Type: replace Abstract: Recent advances in continuous conditional generative modeling, including Continuous conditional Generative Adversarial Network (CcGAN) and Continuous Conditional Diffusion Model (CCDM), estimate high-dimensional data distributions conditioned on scalar regression labels such as angles, ages, or temperatures.
By Xin Ding, Yun Chen, Yongwei Wang, Kao Zhang, Sen Zhang, Peibei Cao, Xiangxue Wang
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
arXiv:2606. 02134v1 Announce Type: cross Abstract: Deep neural networks achieve strong performance on many supervised learning tasks but remain vulnerable to adversarial perturbations.
By Konstantin Kaulen, Hadar Shavit, Holger H. Hoos
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
arXiv:2608. 09314v1 Announce Type: new Abstract: In a federated learning setup for GANs, several adversarial attacks are possible.
By Panav Shah, Avishek Ghosh
In a federated learning setup for GANs, several adversarial attacks are possible. One such attack is label flipping, in which malicious clients deliberately alter label information during local training in order to manipulate the global generator.
arXiv:2602. 05833v2 Announce Type: replace Abstract: There is a need for synthetic training and test datasets that replicate statistical distributions of original datasets without compromising their confidentiality.
By Laura Plein, Alexi Turcotte, Arina Hallemans, Andreas Zeller
SAGE (Subpopulation-Aware Generative Enhancement) is a two-stage generative augmentation framework designed to mitigate spurious correlations in machine learning when group labels are unavailable. It uses cluster-derived sub-labels and class labels to fine‑tune a conditional generative model and text encoder, producing synthetic data that fills underrepresented regions and creates a balanced validation set for last‑layer reweighting. Experiments show SAGE improves worst‑group accuracy to 89.5%, 85.7%, and 79.1% on Waterbirds, CelebA, and MetaShift, outperforming existing group‑label‑free baselines by up to 7.7 percentage points.
By Yiming Luo, Rongqiang Zhao, Jie Liu
arXiv:2606. 01746v1 Announce Type: cross Abstract: Modern neural networks are highly susceptible to adversarial perturbations.
By Kai Wang