|
--- |
|
license: cc-by-sa-3.0 |
|
task_categories: |
|
- image-classification |
|
language: |
|
- en |
|
pretty_name: mnist_ambigous |
|
size_categories: |
|
- 10K<n<100K |
|
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. |
|
|
|
|
|
|
