{"url":"/dataset/bffhq","name":"bFFHQ","full_name":"Gender-biased FFHQ dataset","description_markdown":"Gender-biased FFHQ dataset (bFFHQ) has age as a target label and gender as a correlated bias, and the images are from the FFHQ dataset. The images include the dominant number of young women (i.e., aged 10-29) and old men (i.e., aged 40-59) in the training data.\r\n\r\nSource: [Learning Debiased Representation via Disentangled Feature Augmentation](https://github.com/kakaoenterprise/Learning-Debiased-Disentangled)\r\nImage Source: [FFHQ](https://github.com/NVlabs/ffhq-dataset)","description_withheld":null,"homepage":"","introduced_date":"2021-08-23","introduced_date_note":null,"introduced_by":{"paper":"/paper/biaswap-removing-dataset-bias-with-bias","title":"BiaSwap: Removing dataset bias with bias-tailored swapping augmentation","first_author":"Eungyeup Kim","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Facial Attribute Classification","url":"/task/facial-attribute-classification","datasets_with_task":"/datasets/task/facial-attribute-classification"}],"languages":[],"variants":["bFFHQ"],"data_loaders":[],"num_papers_in_archive":18,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/facial-attribute-classification-on-bffhq","task":"Facial Attribute Classification","dataset_variant":"bFFHQ","rows":3,"metrics":["Bias-Conflicting Accuracy"],"first_row_in_archive_order":{"model":"DebiAN","paper":"/paper/discover-and-mitigate-unknown-biases-with","metrics":{"Bias-Conflicting Accuracy":"62.8"},"code_links":[{"title":"zhihengli-UR/DebiAN","url":"https://github.com/zhihengli-UR/DebiAN"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/efficient-debiasing-with-contrastive-weight","title":"Training Debiased Subnetworks with Contrastive Weight Pruning","date":"2022-10-11","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/discover-and-mitigate-unknown-biases-with","title":"Discover and Mitigate Unknown Biases with Debiasing Alternate Networks","date":"2022-07-20","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":2,"samples_unverified":7,"pointer_only_for_licence":9,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/biaswap-removing-dataset-bias-with-bias","title":"BiaSwap: Removing dataset bias with bias-tailored swapping augmentation","date":"2021-08-23","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":9,"samples_ran":2,"samples_unverified":7,"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."}