Update README.md

README.md

@@ -11,4 +11,55 @@ source_datasets:
 - extended|mnist
 annotations_creators:
 - machine-generated
----
+---
+
+
+## Mnist-Ambiguous
+
+This dataset contains mnist-like images, but with an unclear ground truth. For each image, there are two classes which could be considered true.
+Robust and uncertainty-aware DNNs should thus detect and flag these issues.
+
+### Features
+Same as mnist, the supervised dataset has an `image` (28x28 int array) and a `label` (int).
+
+Additionally, the following features are exposed for your convenience:
+
+- `text_label` (str): A textual representation of the probabilistic label, e.g. `p(Pullover)=0.54, p(Shirt)=0.46` 
+- `p_label` (list of floats): Ground-Truth probabilities for each class (two nonzero values for our ambiguous images)
+- `is_ambiguous` (bool): Flag indicating if this is one of our ambiguous images (see 'splits' below)
+
+### Splits
+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
+
+For research targeting explicitly aleatoric uncertainty, we recommend training the model using `train_mixed`. 
+Otherwise, our `test` set will lead to both epistemic and aleatoric uncertainty. 
+Note that in related literature, these 'mixed' splits are sometimes denoted as *dirty* splits.
+
+### Assessment and Validity
+For a brief discussion of the strength and weaknesses of this dataset, 
+including a quantitative comparison to the (only) other ambiguous datasets available in the literature, we refer to our paper.
+
+### Paper
+Pre-print here: [https://arxiv.org/abs/2207.10495](https://arxiv.org/abs/2207.10495)
+
+Citation:
+```
+@misc{https://doi.org/10.48550/arxiv.2207.10495,
+  doi = {10.48550/ARXIV.2207.10495},
+  url = {https://arxiv.org/abs/2207.10495},
+  author = {Weiss, Michael and Gómez, André García and Tonella, Paolo},
+  title = {A Forgotten Danger in DNN Supervision Testing: Generating and Detecting True Ambiguity},
+  publisher = {arXiv},
+  year = {2022}
+}
+```
+
+### License
+As this is a derivative work of mnist, which is CC-BY-SA 3.0 licensed, our dataset is released using the same license.
+
+