328 citations · 418 across the 8 of their papers we have counts for
8 papers
Random Erasing vs. Model Inversion: A Promising Defense or a False Hope?
Viet-Hung Tran, Ngoc-Bao Nguyen, Son T. Mai +4
Model Inversion (MI) attacks pose a significant privacy threat by reconstructing private training data from machine learning models. While existing defenses primarily concentrate o…
CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP
Andreas Fürst, Elisabeth Rumetshofer, Johannes Lehner +8
CLIP yielded impressive results on zero-shot transfer learning tasks and is considered as a foundation model like BERT or GPT3. CLIP vision models that have a rich representation a…
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…
Variational Bayes Inference in Digital Receivers
Viet Hung Tran
The digital telecommunications receiver is an important context for inference methodology, the key objective being to minimize the expected loss function in recovering the transmit…
Bayesian inference for PCA and MUSIC algorithms with unknown number of sources
Viet Hung Tran, Wenwu Wang
Principal component analysis (PCA) is a popular method for projecting data onto uncorrelated components in lower dimension, although the optimal number of components is not specifi…
Copula Variational Bayes inference via information geometry
Viet Hung Tran
Variational Bayes (VB), also known as independent mean-field approximation, has become a popular method for Bayesian network inference in recent years. Its application is vast, e.g…