GAN-based Gaussian Mixture Model Responsibility Learning

no code implementations • 25 Sep 2019 • Wanming Huang, Shuai Jiang, Xuan Liang, Ian Oppermann, Richard Yi Da Xu

Instead of defining p(x|k, θ) explicitly, we devised a modified GAN to allow us to define the distribution using p(z|k, θ), where z is the corresponding latent representation of x, as well as p(k|x, θ) through an additional classification network which is trained with the GAN in an “end-to-end” fashion.

Generative Adversarial Network

Realistic Image Generation using Region-phrase Attention

no code implementations • 4 Feb 2019 • Wanming Huang, Yida Xu, Ian Oppermann

The true-grid region is derived using a set of auxiliary bounding boxes.

Generative Adversarial Network Image Generation +2