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mweiss
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mnist_ambiguous

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Image Classification

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mweiss commited on Mar 16, 2023

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@@ -34,7 +34,7 @@ We provide four splits:
 - `test`: 10'000 ambiguous images
 - `train`: 10'000 ambiguous images - adding ambiguous images to the training set makes sure test-time ambiguous images are in-distribution.
 - `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.,
-- `train_mixed`: 70'000 images, consisting
 Note that the ambiguous train images are highly ambiguous (i.e., the two classes have very similar ground truth likelihoods),
 the training set images allow for more unbalanced ambiguity.

 - `test`: 10'000 ambiguous images
 - `train`: 10'000 ambiguous images - adding ambiguous images to the training set makes sure test-time ambiguous images are in-distribution.
 - `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.,
+- `train_mixed`: 70'000 images, consisting of the (shuffled) concatenation of our ambiguous `training` and the nominal training set.
 Note that the ambiguous train images are highly ambiguous (i.e., the two classes have very similar ground truth likelihoods),
 the training set images allow for more unbalanced ambiguity.