Unifying Generative Models with GFlowNets

Dinghuai Zhang, Ricky T. Q. Chen, Nikolay Malkin, Yoshua Bengio

Abstract: There are many frameworks for deep generative modeling, each often presented with their own specific training algorithms and inference methods. We present a short note on the connections between existing deep generative models and the GFlowNet framework, shedding light on their overlapping traits and providing a unifying viewpoint through the lens of learning with Markovian trajectories. This provides a means for unifying training and inference algorithms, and provides a route to construct an agglomeration of generative models.