{"url":"/sota/image-generation-on-clevr","task":{"name":"Image Generation","url":"/task/image-generation","note":null},"dataset":{"name":"CLEVR","url":"/dataset/clevr"},"category":"Computer Vision","categories":["Computer Vision","Medical","Miscellaneous","Natural Language Processing"],"category_note":null,"description":"**Image Generation** (synthesis) is the task of generating new images from an existing dataset.\r\n\r\n- **Unconditional generation** refers to generating samples unconditionally from the dataset, i.e. $p(y)$\r\n- **[Conditional image generation](/task/conditional-image-generation)** (subtask) refers to generating samples conditionally from the dataset, based on a label, i.e. $p(y|x)$.\r\n\r\nIn this section, you can find state-of-the-art leaderboards for **unconditional generation**. For conditional  generation, and other types of image generations, refer to the subtasks.\r\n\r\n<span style=\"color:grey; opacity: 0.6\">( Image credit: [StyleGAN](https://github.com/NVlabs/stylegan) )</span>","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["FID-5k-training-steps"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"FID-5k-training-steps":"lower"}},"counts":{"rows":6,"rows_with_code":6,"rows_with_paper_page":6,"rows_dated":6,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"Projected GAN","metrics":{"FID-5k-training-steps":"0.89"},"uses_additional_data":false,"paper_date":"2021-11-01","paper":"/paper/projected-gans-converge-faster","paper_url":"https://arxiv.org/abs/2111.01007v1","paper_title":"Projected GANs Converge Faster","code":"https://github.com/autonomousvision/projected_gan","n_code_links":3,"syntology":{"n_ran":38,"n_unverified":11,"n_samples":49,"n_pointer_only_licence":6}},{"rank_in_archive_order":2,"model":"GANformer","metrics":{"FID-5k-training-steps":"9.1679"},"uses_additional_data":false,"paper_date":"2021-03-01","paper":"/paper/generative-adversarial-transformers","paper_url":"https://arxiv.org/abs/2103.01209v4","paper_title":"Generative Adversarial Transformers","code":"https://github.com/dorarad/gansformer","n_code_links":2,"syntology":{"n_ran":5,"n_unverified":1,"n_samples":6,"n_pointer_only_licence":0}},{"rank_in_archive_order":3,"model":"StyleGAN2","metrics":{"FID-5k-training-steps":"16.0534"},"uses_additional_data":false,"paper_date":"2021-03-01","paper":"/paper/generative-adversarial-transformers","paper_url":"https://arxiv.org/abs/2103.01209v4","paper_title":"Generative Adversarial Transformers","code":"https://github.com/dorarad/gansformer","n_code_links":2,"syntology":{"n_ran":5,"n_unverified":1,"n_samples":6,"n_pointer_only_licence":0}},{"rank_in_archive_order":4,"model":"GAN","metrics":{"FID-5k-training-steps":"25.0244"},"uses_additional_data":false,"paper_date":"2021-03-01","paper":"/paper/generative-adversarial-transformers","paper_url":"https://arxiv.org/abs/2103.01209v4","paper_title":"Generative Adversarial Transformers","code":"https://github.com/dorarad/gansformer","n_code_links":2,"syntology":{"n_ran":5,"n_unverified":1,"n_samples":6,"n_pointer_only_licence":0}},{"rank_in_archive_order":5,"model":"SAGAN","metrics":{"FID-5k-training-steps":"26.0433"},"uses_additional_data":false,"paper_date":"2021-03-01","paper":"/paper/generative-adversarial-transformers","paper_url":"https://arxiv.org/abs/2103.01209v4","paper_title":"Generative Adversarial Transformers","code":"https://github.com/dorarad/gansformer","n_code_links":2,"syntology":{"n_ran":5,"n_unverified":1,"n_samples":6,"n_pointer_only_licence":0}},{"rank_in_archive_order":6,"model":"VQGAN","metrics":{"FID-5k-training-steps":"32.6031"},"uses_additional_data":false,"paper_date":"2021-03-01","paper":"/paper/generative-adversarial-transformers","paper_url":"https://arxiv.org/abs/2103.01209v4","paper_title":"Generative Adversarial Transformers","code":"https://github.com/dorarad/gansformer","n_code_links":2,"syntology":{"n_ran":5,"n_unverified":1,"n_samples":6,"n_pointer_only_licence":0}}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":6,"rows_with_any_sample_ran":6,"distinct_papers_with_graph_line":2,"distinct_papers_with_any_sample_ran":2,"samples_over_distinct_papers":{"n_ran":43,"n_unverified":12,"n_samples":55,"n_pointer_only_licence":6,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":63,"n_unverified":16,"n_samples":79,"n_pointer_only_licence":6,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}