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This paper shows that two commonly used evaluation metrics for generative models, the Fr\'echet Inception Distance (FID) and the Inception Score (IS), are biased -- the expected value of the score computed for a finite sample set is not the true value of the score.
A note on the generation of random normal deviates
George EP Box · 1958
Earlier work this paper cites.
Quadrature and interpolation formulas for tensor products of certain classes of functions
Sergei Abramovich Smolyak · 1963
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Algorithm 247: Radical-inverse quasi-random point sequence
John H Halton · 1964
Earlier work this paper cites.
On the distribution of points in a cube and the approximate evaluation of integrals
Il’ya Meerovich Sobol’ · 1967
Earlier work this paper cites.
Options: A monte carlo approach
Phelim P Boyle · 1977
Earlier work this paper cites.
Randomization of lattice rules for numerical multiple integration
Stephen Joe · 1990
Earlier work this paper cites.
Randomly permuted (t, m, s)-nets and (t, s)-sequences
Art B Owen · 1995
Earlier work this paper cites.
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Henrik Wann Jensen · 1996
Earlier work this paper cites.
Faster valuation of financial derivatives
Spassimir Paskov and Joseph F Traub · 1996
Earlier work this paper cites.
Stochastic simulation: algorithms and analysis
Søren Asmussen and Peter W Glynn · 2007
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky et al · 2009
Earlier work this paper cites.
Higher order scrambled digital nets achieve the optimal rate of the root mean square error for smooth integrands
Josef Dick et al · 2011
Earlier work this paper cites.
Generating low-discrepancy sequences from the normal distribution: Box–muller or inverse transform?
Giray Ökten and Ahmet Göncü · 2011
Earlier work this paper cites.
High-dimensional integration: the quasi-monte carlo way
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