41 citations · 50 across the 3 of their papers we have counts for
6 papers
Do Vision-Language Models Leak What They Learn? Adaptive Token-Weighted Model Inversion Attacks
Ngoc-Bao Nguyen, Sy-Tuyen Ho, Koh Jun Hao +1
Model inversion (MI) attacks pose significant privacy risks by reconstructing private training data from trained neural networks. While prior studies have primarily examined unimod…
Synth-Align: Improving Trustworthiness in Vision-Language Model with Synthetic Preference Data Alignment
Robert Wijaya, Ngoc-Bao Nguyen, Ngai-Man Cheung
Large Vision-Language Models (LVLMs) have shown promising capabilities in understanding and generating information by integrating both visual and textual data. However, current mod…
On the Vulnerability of Skip Connections to Model Inversion Attacks
Jun Hao Koh, Sy-Tuyen Ho, Ngoc-Bao Nguyen +1
Skip connections are fundamental architecture designs for modern deep neural networks (DNNs) such as CNNs and ViTs. While they help improve model performance significantly, we iden…
On Data Augmentation for GAN Training
Ngoc-Trung Tran, Viet-Hung Tran, Ngoc-Bao Nguyen +2
Recent successes in Generative Adversarial Networks (GAN) have affirmed the importance of using more data in GAN training. Yet it is expensive to collect data in many domains such…
Self-supervised GAN: Analysis and Improvement with Multi-class Minimax Game
Ngoc-Trung Tran, Viet-Hung Tran, Ngoc-Bao Nguyen +2
Self-supervised (SS) learning is a powerful approach for representation learning using unlabeled data. Recently, it has been applied to Generative Adversarial Networks (GAN) traini…
An Improved Self-supervised GAN via Adversarial Training
Ngoc-Trung Tran, Viet-Hung Tran, Ngoc-Bao Nguyen +1
We propose to improve unconditional Generative Adversarial Networks (GAN) by training the self-supervised learning with the adversarial process. In particular, we apply self-superv…