Divergence Frontiers for Evaluating Deep Generative Models
I was reading a collection of interesting papers on the evaluation of deep generative models, which I have summarised in the following slides . The papers include: Assessing Generative Models via Precision and Recall " (NeurIPS 2018) Precision-Recall Curves Using Information Divergence Frontiers (AISTATS 2020) Divergence Frontiers for Generative Models:Sample Complexity, Quantization Effects,and Frontier Integrals (NuerIPS 2021) MAUVE: Measuring the GapBetween Neural Text and Human Textusing Divergence Frontiers (NuerIPS 2021)