#generative models
32 papers match
Error Analysis of Neural-Network-Based Engression
Juntong Chen, Zijian Guo, Xinwei Shen
The paper analyzes the theoretical error of neural‑network‑based engression, a method for learning conditional distributions via an energy score, and derives convergence rates by d…
Forecasting Land Art Under Climate Scenarios
Alev Cinbarci, Sean Kalaycioglu
The paper builds a two‑stage pipeline to forecast visual complexity of the Spiral Jetty land artwork under future climate scenarios, using climate model outputs, statistical regres…
RIPPLE: Generating Multi-Channel Phase, Not Recovering It
Jaehyuk Lee, Yeajin Lee, Dayeon Shin +1
The paper introduces RIPPLE, a method that generates inter‑channel phase directly using a prior‑based Griffin–Lim approach and rectified flow, improving phase coherence for multi‑c…
Amortized Moment Matching for Visual Generation
Wenze Liu, Xintao Wang, Pengfei Wan +1
The paper introduces amortized moment matching, using neural networks to learn data moments as training signals, and proposes the Amortized Fréchet Distance loss to improve one-ste…
BayesAME: Bayesian Active Model Evaluation
Paula Cordero Encinar, Taylan Cemgil, Arnaud Doucet +2
BayesAME is a Bayesian sequential framework that automatically determines the size of a coreset for evaluating large generative models, using latent ability models and information‑…
Allure of Craquelure: A Variational-Generative Approach to Crack Detection in Paintings
Laura Paul, Holger Rauhut, Martin Burger +2
The paper presents a hybrid method that treats crack detection in digitized paintings as an inverse problem, using a deep generative model to represent the crack-free artwork and a…