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README.md
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All possible combinations of these factors are present exactly once, generating a total of **737,280 images** at a resolution of **64×64 pixels**. The ground-truth latent factors are provided for each image, both as **discrete classes** and **continuous values**. The dataset is specifically designed for assessing the ability of models to learn **disentangled representations**, and has been used in many follow-up works after β-VAE.
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All possible combinations of these factors are present exactly once, generating a total of **737,280 images** at a resolution of **64×64 pixels**. The ground-truth latent factors are provided for each image, both as **discrete classes** and **continuous values**. The dataset is specifically designed for assessing the ability of models to learn **disentangled representations**, and has been used in many follow-up works after β-VAE.
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The dataset is commonly used for **benchmarking disentanglement learning**, and can be used in conjunction with other variants:
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- [randall-lab/dsprites-color](https://huggingface.co/datasets/randall-lab/dsprites-color)
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- [randall-lab/dsprites-noisy](https://huggingface.co/datasets/randall-lab/dsprites-noisy)
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- [randall-lab/dsprites-scream](https://huggingface.co/datasets/randall-lab/dsprites-scream)
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## Dataset Source
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- **Homepage**: [https://github.com/google-deepmind/dsprites-dataset](https://github.com/google-deepmind/dsprites-dataset)
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