{"url":"/dataset/pick-a-filter","name":"Pick-a-Filter","full_name":null,"description_markdown":"Pick-a-Filter is a semi-synthetic dataset constructed from [Pick-a-Pic v1](https://huggingface.co/datasets/yuvalkirstain/pickapic_v1) to measure the capability of text-to-image models of adapting to heterogeneous preferences. We assign users from V1 randomly into two groups: those who prefer blue, cooler image tones (G1) and those who prefer red, warmer image tones (G2). After constructing this split, we apply the following logic to construct the dataset:\r\n\r\n1. Apply “winning” and “losing” filters to appropriate images depending on label. For G1 the winning filter is blue, and for G2 the winning filter is red.\r\n2. Randomly shortlist β % of samples to add filters. The remaining (1 − β) % of samples will remain unaltered (default images from Pick-a-Pic v1). The hyperparameter β is called the **mixture ratio**.\r\n3. Randomly select 50% of above-shortlisted samples to apply a filter to only the winning image, and the remaining 50% to apply a filter to only losing image\r\n\r\nWe add these sources of randomness to make learning preferences on Pick-a-Filter less prone to hacking (e.g. the model\r\ncould trivially learn to predict an image with a filter as the preferred image).\r\n\r\nWe provide the [OpenCLIP H/14](https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K) embeddings of all versions of Pick-a-Filter used in PAL in this repository, with β varying from 0.0 (no filtering) to 1.0 (all images are filtered). For example, pick-a-filter with β = 0.4 is available in `paf_0.4_mix_ratio.zip`.","description_withheld":null,"homepage":"https://huggingface.co/datasets/ramya-ml/pick-a-filter-embeds","introduced_date":"2024-06-12","introduced_date_note":null,"introduced_by":{"paper":"/paper/pal-pluralistic-alignment-framework-for","title":"PAL: Pluralistic Alignment Framework for Learning from Heterogeneous Preferences","first_author":"Daiwei Chen","url":null},"license":{"name":"MIT","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Text-to-Image Generation","url":"/task/text-to-image-generation","datasets_with_task":"/datasets/task/text-to-image-generation"}],"languages":[],"variants":["Pick-a-Filter"],"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."}