Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier Integrals Lang Liu 1 Krishna Pillutla 2 Sean Welleck 2,3 Sewoong Oh

The spectacular success of deep generative models calls for quantitative tools to measure their statistical performance. Divergence frontiers have recently been proposed as an evaluation framework for generative models, due to their ability to measure the quality-diversity trade-off inherent to deep generative modeling.