1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.AI2023
Controllable Path of Destruction
Matthew Siper, Sam Earle, Zehua Jiang +2
Path of Destruction (PoD) is a self-supervised method for learning iterative generators. The core idea is to produce a training set by destroying a set of artifacts, and for each d…
cs.CV2022★ 1 cited
Diversity and Novelty MasterPrints: Generating Multiple DeepMasterPrints for Increased User Coverage
M Charity, Nasir Memon, Zehua Jiang +2
This work expands on previous advancements in genetic fingerprint spoofing via the DeepMasterPrints and introduces Diversity and Novelty MasterPrints. This system uses quality dive…
cs.AI2022
Learning Controllable 3D Level Generators
Zehua Jiang, Sam Earle, Michael Cerny Green +1
Procedural Content Generation via Reinforcement Learning (PCGRL) foregoes the need for large human-authored data-sets and allows agents to train explicitly on functional constraint…