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Elements

Introduced by Angus Nicolson et al. in Explaining Explainability: Recommendations for Effective Use of Concept Activation Vectors4 Apr 2024 archive 2025-07-28

A configurable synthetic dataset of simple shapes with ground truth concepts and known causal relationships between concepts and classes.

The dataset does not consist of a specific set of images, instead it is provided as code to generate images matching the dataset. The specific combination of shapes, colours, textures and number of objects in each image is configurable, along with the definitions of each class.

The dataset is intended to be used to help study concept-based interpretability methods as it gives you full control over the concepts that have a causal influence on the class and over the associations between concepts within the dataset.

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 1 paper for it but never published that list.

Dataset loaders archive 2025-07-28

No loader listed in the archive.

Tasks archive 2025-07-28

No task tagged in the archive.

License archive 2025-07-28

MIT

Modalities archive 2025-07-28

Languages archive 2025-07-28

No language tagged.

Variants archive 2025-07-28

  • Elements

1 variant name, as the archive lists them.

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