PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation

arXiv:2402.04355 · stat.ML, cs.AI, cs.LG, stat.ME · Submitted 2024-02-06 · Read on arXiv

stat.ML, cs.AI, cs.LG, stat.ME

Submitted: 2024-02-06

Updated: 2026-08-31

Code: https://github.com/Ciela-Institute/PQM

Terminology

• Generative Model — 50× in this paper · explained in 2 episodes
• Generative Models — 33× in this paper · explained in 2 episodes
• Diffusion Model — 19× in this paper · explained in 8 episodes
• Fidelity — 14× in this paper · explained in 2 episodes
• Adam — 8× in this paper · explained in 2 episodes
• Diffusion Models — 8× in this paper · explained in 6 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Neural Information Processing Systems — 5× in this paper
• Feature Extraction — 4× in this paper · explained in 2 episodes
• International Conference on Learning Representations — 4× in this paper
• Inverse Problem — 4× in this paper · explained in 2 episodes
• Computer Vision and Pattern Recognition — 3× in this paper
• Generalization — 3× in this paper · explained in 13 episodes
• Normalizing Flow — 3× in this paper · explained in 3 episodes
• Normalizing Flows — 3× in this paper · explained in 4 episodes
• Wasserstein Distance — 3× in this paper · explained in 4 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Ablation study — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• International Conference on Machine Learning — 2× in this paper
• James Webb Space Telescope — 2× in this paper
• 2-Wasserstein Distance — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Simulation-based Inference — 1× in this paper · explained in 2 episodes
• Uncertainty Quantification — 1× in this paper · explained in 8 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes

Sources

• Event-horizon-scale Imaging of M87* under Different Assumptions via Deep Generative Image Priors
• Denoising Diffusion Delensing Delight: Reconstructing the Non-Gaussian CMB Lensing Potential with Diffusion Models
• Tackling the Problem of Distributional Shifts: Correcting Misspecified, High-Dimensional Data-Driven Priors for Inverse Problems
• Diffusion Models for Probabilistic Deconvolution of Galaxy Images

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