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Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models George Stein Jesse C. Cresswell Rasa Hosseinzadeh Yi Sui Brendan Leigh Ross
We address these flaws through a study of alternative self-supervised feature extractors, find that the semantic information encoded by individual networks strongly depends on their training procedure, and show that DINOv2-ViT -L/14 allows for much richer evaluation of generative models.
GeometricAll-WayBooleanTensorDecomposition APPENDIX
Weshowtheperformance ofGETF onhigh order tensor data inFigure 6. Noted, when theorder increases, the tensor size would increase exponentially.
194b8dac525581c346e30a2cebe9a369-Supplemental-Conference.pdf
Further, we clarify that the term 'self-supervised' has different meanings in DA and in pretraining [S23, S24, S25, S26]. Problem definitions for both papers are different.
Self-SupervisedContrastivePre-TrainingforTime SeriesviaTime-FrequencyConsistency
To address this challenge, methods need to accommodate target domains with different temporal dynamics and be capable of doing so without seeing any target examples during pre-training.
