122 citations · 239 across the 37 of their papers we have counts for
5 papers · 1 filter
KGAN: How to Break The Minimax Game in GAN
Trung Le, Tu Dinh Nguyen, Dinh Phung
Generative Adversarial Networks (GANs) were intuitively and attractively explained under the perspective of game theory, wherein two involving parties are a discriminator and a gen…
Analogical-based Bayesian Optimization
Trung Le, Khanh Nguyen, Tu Dinh Nguyen +1
Some real-world problems revolve to solve the optimization problem \max_{x\in\mathcal{X}}f\left(x\right) where f\left(.\right) is a black-box function and X might be the set of non…
Dual Discriminator Generative Adversarial Nets
Tu Dinh Nguyen, Trung Le, Hung Vu +1
We propose in this paper a novel approach to tackle the problem of mode collapse encountered in generative adversarial network (GAN). Our idea is intuitive but proven to be very ef…
Geometric Enclosing Networks
Trung Le, Hung Vu, Tu Dinh Nguyen +1
Training model to generate data has increasingly attracted research attention and become important in modern world applications. We propose in this paper a new geometry-based optim…
Multi-Generator Generative Adversarial Nets
Quan Hoang, Tu Dinh Nguyen, Trung Le +1
We propose a new approach to train the Generative Adversarial Nets (GANs) with a mixture of generators to overcome the mode collapsing problem. The main intuition is to employ mult…