methodology-9
Two-sample testing
3260 papers • 126 benchmarks • 313 datasets
In statistical hypothesis testing, a two-sample test is a test performed on the data of two random samples, each independently obtained from a different given population. The purpose of the test is to determine whether the difference between these two populations is statistically significant. The statistics used in two-sample tests can be used to solve many machine learning problems, such as domain adaptation, covariate shift and generative adversarial networks.
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Benchmarks
These leaderboards are used to track progress in two-sample-testing-19
Trend
Dataset
Best Model
Actions
HIGGS Data Set
HIGGS Data Set
MNIST vs Fake MNIST
MNIST vs Fake MNIST
Blob (9 modes, 40 for each)
Libraries
iUse these libraries to find two-sample-testing-19 models and implementations
Datasets
Subtasks
No subtasks available.
Most implemented papers
PacGAN: The Power of Two Samples in Generative Adversarial Networks
Sewoong Oh, Zinan Lin, G. Fanti, A. Khetan•Mon Dec 11 2017
It is shown that packing naturally penalizes generators with mode collapse, thereby favoring generator distributions with less mode collapse during the training process, and numerical experiments suggests that packing provides significant improvements in practice as well.
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