Improved Training of Generative Adversarial Networks Using Decision Forests (with Yan Zuo and Gil Avraham)

This paper shows how to use decision forests as the discriminator in a Generative Adversarial Network. The forest's piecewise structure provides a stronger, more stable gradient signal to the generator than a standard CNN discriminator, improving sample diversity and training stability across CIFAR-10, STL-10 and CelebA without any change to the generator architecture.

[WACV 2021 paper]