The past few years have seen impressive progress in the development of deep generative models capable of producing high-dimensional, complex, and photo-realistic data. However, current methods for evaluating such models remain incomplete: standard likelihood-based metrics do not always apply and rarely correlate with perceptual fidelity, while sample-based metrics, such as FID, are insensitive to overfitting, i.e., inability to generalize beyond the training set. To address these limitations, we propose a new metric called the Feature Likelihood Divergence (FLD), a parametric sample-based metric that uses density estimation to provide a comprehensive trichotomic evaluation accounting for novelty (i.e., different from the training samples), fidelity, and diversity of generated samples. We empirically demonstrate the ability of FLD to identify overfitting problem cases, even when previously proposed metrics fail. We also extensively evaluate FLD on various image datasets and model classes, demonstrating its ability to match intuitions of previous metrics like FID while offering a more comprehensive evaluation of generative models. Code is available at https://github.com/marcojira/fld.
Unified Perspective on Probability Divergence via Maximum Likelihood Density Ratio Estimation: Bridging KL-Divergence and Integral Probability Metrics
The University of Tokyo（东京大学）；CyberAgent, Inc.（赛博艾金特公司）；RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）；Preferred Networks, Inc.（优选网络公司）
Nonparametric likelihood-free inference with Jensen-Shannon divergence for simulator-based models with categorical output
Helsinki Institute of Information Technology (HIIT)（赫尔辛基信息技术研究所）；University of Helsinki（赫尔辛基大学）；University of Oslo（奥斯陆大学）；Wellcome Sanger Institute（惠康桑格研究所）；KTH Royal Institute of Technology（瑞典皇家理工学院）
StAD: Stein Amortized Divergence for Fast Likelihoods with Diffusion and Flow
Institute of Astronomy（天文研究所）；Kavli Institute for Cosmology, University of Cambridge, Cambridge, UK（剑桥大学卡文迪许实验室，剑桥，英国）；Research Computing Services, University of Cambridge, Cambridge, UK（剑桥大学研究计算服务）；The Oskar Klein Centre, Department of Physics, Stockholm University, Stockholm, SE（斯德哥尔摩大学物理系奥斯卡·克莱因中心）；Cavendish Laboratory, Department of Physics, University of Cambridge, Cambridge, UK（剑桥大学物理系卡文迪许实验室）
Scalable Inference-Time Annealing with Surrogate Likelihood Estimators
CMU-Pitt PhD Program in Computational Biology Dept. of Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA 15260, USA（卡内基梅隆大学-匹兹堡联合博士项目 计算生物学部门 计算与系统生物学系，匹兹堡大学，匹兹堡，PA 15260，USA）
Courant Institute of Mathematical Sciences（库朗数学科学研究所）；New York University（纽约大学）；Memorial Sloan Kettering Cancer Center（纪念斯隆·凯特琳癌症中心）；Center for Data Science（数据科学中心）
Generalization Bounds for Quantum Learning via Rényi Divergences
Indian Statistical Institute, Kolkata（印度统计研究所，加尔各答）；School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）；International Quantum Academy, Futian District, Shenzhen（深圳国际量子 academy，福田区）；Graduate School of Mathematics, Nagoya University（名古屋大学数学研究生院）
