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extended|mnist
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mweiss commited on
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Update README.md

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@@ -33,8 +33,8 @@ We provide four splits:
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  - `test`: 10'000 ambiguous images
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  - `train`: 10'000 ambiguous images - adding ambiguous images to the training set makes sure test-time ambiguous images are in-distribution.
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- - `test_mixed`: 20'000 images, consisting of the (shuffled) concatenation of our ambiguous `test` test and the nominal mnist test set by LeCun et. al.,
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- - `train_mixed`: 70'000 images, consisting
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  Note that the ambiguous train images are highly ambiguous (i.e., the two classes have very similar ground truth likelihoods),
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  the training set images allow for more unbalanced ambiguity.
 
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  - `test`: 10'000 ambiguous images
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  - `train`: 10'000 ambiguous images - adding ambiguous images to the training set makes sure test-time ambiguous images are in-distribution.
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+ - `test_mixed`: 20'000 images, consisting of the (shuffled) concatenation of our ambiguous `test` set and the nominal *original* fashion mnist test set
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+ - `train_mixed`: 70'000 images, consisting of the (shuffled) concatenation of our ambiguous `training` and the nominal training set.
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  Note that the ambiguous train images are highly ambiguous (i.e., the two classes have very similar ground truth likelihoods),
40
  the training set images allow for more unbalanced ambiguity.