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cs.LG2023★ 21 cited
Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
George Stein, Jesse C. Cresswell, Rasa Hosseinzadeh +7
We systematically study a wide variety of generative models spanning semantically-diverse image datasets to understand and improve the feature extractors and metrics used to evalua…
cs.LG2020★ 22 cited
Unsupervised in-distribution anomaly detection of new physics through conditional density estimation
George Stein, Uros Seljak, Biwei Dai
Anomaly detection is a key application of machine learning, but is generally focused on the detection of outlying samples in the low probability density regions of data. Here we in…