{"url":"/dataset/idsprites","name":"idsprites","full_name":"Infinite dSprites","description_markdown":"Easily generate simple continual learning benchmarks. Inspired by dSprites.\r\n\r\n> It allows for procedurally generating a virtually infinite progression of random two-dimensional shapes. [...] we generate each unique shape in every combination of orientation, scale, and position. Most importantly, by providing the ground truth values of individual factors of variation (FoVs)","description_withheld":null,"homepage":"https://github.com/sbdzdz/idsprites","introduced_date":"2023-12-27","introduced_date_note":null,"introduced_by":{"paper":"/paper/disentangled-continual-learning-separating","title":"Infinite dSprites for Disentangled Continual Learning: Separating Memory Edits from Generalization","first_author":"Sebastian Dziadzio","url":null},"license":{"name":"MIT","url":"https://github.com/sbdzdz/idsprites/blob/main/LICENSE"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Image Classification","url":"/task/image-classification","datasets_with_task":"/datasets/task/image-classification"},{"name":"Continual Learning","url":"/task/continual-learning","datasets_with_task":"/datasets/task/continual-learning"}],"languages":[],"variants":["idsprites"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"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."}