Gradient Flow Drifting: Generative Modeling via Wasserstein Gradient Flows of KDE-Approximated Divergences
Gradient Flow Drifting: Generative Modeling via Wasserstein Gradient Flows of KDE-Approximated Divergences explores A new family of generative models leveraging Wasserstein gradient flows for improved performance in avoiding mode collapse.. Commercial viability score: 4/10 in Generative Modeling.
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