{"url":"/dataset/natural-sprites","name":"Natural Sprites","full_name":null,"description_markdown":"This csv consists of (x-position, y-position, area) tuples of three views (left, middle, right) of downscaled binary masks with aspect ratio kept (64 x 128) from the 2019 YouTube-VIS challenge, which can be found at https://competitions.codalab.org/competitions/20127#participate-get-data. Extracting pairs from this csv results in 234,652 transitions in the given statistics. These statistics can be used to augment ground truth factor distributions with natural transitions, which we demonstrate with spriteworld. For details, we refer to our paper, which can be found at https://openreview.net/forum?id=EbIDjBynYJ8.","description_withheld":null,"homepage":"https://zenodo.org/record/3948069#.YgWiH_XMKbg","introduced_date":"2020-07-21","introduced_date_note":null,"introduced_by":{"paper":"/paper/towards-nonlinear-disentanglement-in-natural","title":"Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding","first_author":"David Klindt","url":null},"license":{"name":"Creative Commons Attribution 4.0 International","url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Disentanglement","url":"/task/disentanglement","datasets_with_task":"/datasets/task/disentanglement"}],"languages":[],"variants":["Natural Sprites"],"data_loaders":[{"repo":"https://github.com/bethgelab/slow_disentanglement","url":"https://github.com/bethgelab/slow_disentanglement","frameworks":["pytorch"]}],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/disentanglement-on-natural-sprites","task":"Disentanglement","dataset_variant":"Natural Sprites","rows":1,"metrics":["MCC"],"first_row_in_archive_order":{"model":"SlowVAE","paper":"/paper/towards-nonlinear-disentanglement-in-natural","metrics":{"MCC":"52.6"},"code_links":[{"title":"bethgelab/slow_disentanglement","url":"https://github.com/bethgelab/slow_disentanglement"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/towards-nonlinear-disentanglement-in-natural","title":"Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding","date":"2020-07-21","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":1,"samples_unverified":3,"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":1,"samples_harvested":4,"samples_ran":1,"samples_unverified":3,"pointer_only_for_licence":0,"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."}