{"url":"/dataset/mnist-m","name":"MNIST-M","full_name":null,"description_markdown":"**MNIST-M** is created by combining MNIST digits with the patches randomly extracted from color photos of BSDS500 as their background. It contains 59,001 training and 90,001 test images.\r\n\r\nSource: [A Review of Single-Source Deep Unsupervised Visual Domain Adaptation](https://arxiv.org/abs/2009.00155)\r\nImage Source: [https://arxiv.org/pdf/1505.07818v4.pdf](https://arxiv.org/pdf/1505.07818v4.pdf)","description_withheld":null,"homepage":"https://www.kaggle.com/datasets/aquibiqbal/mnistm","introduced_date":"2017-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/domain-adversarial-training-of-neural","title":"Domain-Adversarial Training of Neural Networks","first_author":"Yaroslav Ganin","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Domain Adaptation","url":"/task/domain-adaptation","datasets_with_task":"/datasets/task/domain-adaptation"}],"languages":[],"variants":["MNIST-to-MNIST-M","MNIST-M"],"data_loaders":[{"repo":"https://github.com/mashaan14/MNIST-M","url":"https://github.com/mashaan14/MNIST-M","frameworks":["pytorch"]}],"num_papers_in_archive":193,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/domain-adaptation-on-mnist-to-mnist-m","task":"Domain Adaptation","dataset_variant":"MNIST-to-MNIST-M","rows":5,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"DRANet","paper":"/paper/dranet-disentangling-representation-and","metrics":{"Accuracy":"98.7"},"code_links":[{"title":"Seung-Hun-Lee/DRANet","url":"https://github.com/Seung-Hun-Lee/DRANet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/dranet-disentangling-representation-and","title":"DRANet: Disentangling Representation and Adaptation Networks for Unsupervised Cross-Domain Adaptation","date":"2021-03-24","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/deepjdot-deep-joint-distribution-optimal","title":"DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation","date":"2018-03-27","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":17,"samples_ran":0,"samples_unverified":17,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/domain-separation-networks","title":"Domain Separation Networks","date":"2016-08-22","rows_on_this_dataset":1,"code_links":6,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":14,"samples_ran":5,"samples_unverified":9,"pointer_only_for_licence":6,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/domain-adversarial-training-of-neural","title":"Domain-Adversarial Training of Neural Networks","date":"2015-05-28","rows_on_this_dataset":1,"code_links":37,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":52,"samples_ran":33,"samples_unverified":19,"pointer_only_for_licence":22,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/learning-transferable-features-with-deep","title":"Learning Transferable Features with Deep Adaptation Networks","date":"2015-02-10","rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":5,"samples_harvested":86,"samples_ran":41,"samples_unverified":45,"pointer_only_for_licence":29,"papers_with_no_sample_that_ran":1,"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."}