Datasets › UrbanCars

UrbanCars

Introduced by Zhiheng Li et al. in A Whac-A-Mole Dilemma: Shortcuts Come in Multiples Where Mitigating One Amplifies Others9 Dec 2022 archive 2025-07-28

UrbanCars facilitates multi-shortcut learning under the controlled setting with two shortcuts—background and co-occurring object. The task is classifying the car body type into two categories: urban car and country car. The dataset contains three splits: training, validation, and testing. In the training set, two shortcuts spuriously correlate with the car body type. Both validation and testing sets are balanced, i.e., no spurious correlations. The validation set is used for model selection, and the testing set evaluates the mitigation of two shortcuts.

Benchmarks archive 2025-07-28

No leaderboard in the archive resolves to this dataset.

Papers archive 2025-07-28

No paper in the archive has a leaderboard row on this dataset; the archive counts 26 papers for it but never published that list.

Dataset loaders archive 2025-07-28

1 loader as listed in the archive; links are outbound and not re-checked here.

Tasks archive 2025-07-28

License archive 2025-07-28

CC BY-NC

Modalities archive 2025-07-28

Languages archive 2025-07-28

No language tagged.

Variants archive 2025-07-28

  • UrbanCars

1 variant name, as the archive lists them.

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