{"url":"/dataset/stacked-mnist","name":"Stacked MNIST","full_name":"Stacked MNIST","description_markdown":"The **Stacked MNIST** dataset is derived from the standard MNIST dataset with an increased number of discrete modes. 240,000 RGB images in the size of 32×32 are synthesized by stacking three random digit images from MNIST along the color channel, resulting in 1,000 explicit modes in a uniform distribution corresponding to the number of possible triples of digits.\r\n\r\nSource: [Inclusive GAN: Improving Data and Minority Coverage in Generative Models](https://arxiv.org/abs/2004.03355)\r\nImage Source: [https://arxiv.org/abs/1705.07761](https://arxiv.org/abs/1705.07761)","description_withheld":null,"homepage":"","introduced_date":"2017-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/unrolled-generative-adversarial-networks","title":"Unrolled Generative Adversarial Networks","first_author":"Luke Metz","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Image Generation","url":"/task/image-generation","datasets_with_task":"/datasets/task/image-generation"}],"languages":[],"variants":["Stacked MNIST"],"data_loaders":[],"num_papers_in_archive":43,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/image-generation-on-stacked-mnist","task":"Image Generation","dataset_variant":"Stacked MNIST","rows":3,"metrics":["FID","Inception score"],"first_row_in_archive_order":{"model":"VAEBM","paper":"/paper/vaebm-a-symbiosis-between-variational","metrics":{"FID":"12.96","Inception score":"8.15"},"code_links":[{"title":"NVlabs/VAEBM","url":"https://github.com/NVlabs/VAEBM"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/vaebm-a-symbiosis-between-variational","title":"VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models","date":"2020-10-01","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":6,"samples_unverified":3,"pointer_only_for_licence":9,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/implicit-generation-and-modeling-with-energy","title":"Implicit Generation and Modeling with Energy Based Models","date":"2019-12-01","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/prescribed-generative-adversarial-networks","title":"Prescribed Generative Adversarial Networks","date":"2019-10-09","rows_on_this_dataset":1,"code_links":2,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":9,"samples_ran":6,"samples_unverified":3,"pointer_only_for_licence":9,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}