activity
20182021
most citedSelf-supervised GAN: Analysis and Improvement with Multi-class Minimax Game

41 citations · 51 across the 3 of their papers we have counts for

collaborators

10 papers

cs.CV20211 cited

A Closer Look at Fourier Spectrum Discrepancies for CNN-generated Images Detection

Keshigeyan Chandrasegaran, Ngoc-Trung Tran, Ngai-Man Cheung

CNN-based generative modelling has evolved to produce synthetic images indistinguishable from real images in the RGB pixel space. Recent works have observed that CNN-generated imag…

cs.LG2020

InfoMax-GAN: Improved Adversarial Image Generation via Information Maximization and Contrastive Learning

Kwot Sin Lee, Ngoc-Trung Tran, Ngai-Man Cheung

While Generative Adversarial Networks (GANs) are fundamental to many generative modelling applications, they suffer from numerous issues. In this work, we propose a principled fram…

cs.CV2020

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…

cs.CV201941 cited

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…

cs.CV2019

3D Face Pose and Animation Tracking via Eigen-Decomposition based Bayesian Approach

Ngoc-Trung Tran, Fakhr-Eddine Ababsa, Maurice Charbit +3

This paper presents a new method to track both the face pose and the face animation with a monocular camera. The approach is based on the 3D face model CANDIDE and on the SIFT (Sca…

cs.CV20199 cited

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…