Unifying Generative Models with GFlowNets

• URL: http://arxiv.org/abs/2209.02606v1
• Date: Tue, 6 Sep 2022 15:52:51 GMT
• Title: Unifying Generative Models with GFlowNets
• Authors: Dinghuai Zhang, Ricky T. Q. Chen, Nikolay Malkin, Yoshua Bengio
• Abstract summary: We present a short note on the connections between existing deep generative models and the GFlowNet framework, shedding light on their overlapping traits. This provides a means for unifying training and inference algorithms, and provides a route to construct an agglomeration of generative models.
• Score: 85.38102320953551
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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