{"url":"/sota/semi-supervised-image-classification-on-mini-1","task":{"name":"Semi-Supervised Image Classification","url":"/task/semi-supervised-image-classification","note":null},"dataset":{"name":"Mini-ImageNet, 10000 Labels","url":null},"category":"Computer Vision","categories":["Computer Vision"],"category_note":null,"description":"Semi-supervised image classification leverages unlabelled data as well as labelled data to increase classification performance.\r\n\r\nYou may want to read some blog posts to get an overview before reading the papers and checking the leaderboards:\r\n\r\n- [An overview of proxy-label approaches for semi-supervised learning](https://ruder.io/semi-supervised/) - Sebastian Ruder\r\n- [Semi-Supervised Learning in Computer Vision](https://amitness.com/2020/07/semi-supervised-learning/) - Amit Chaudhary\r\n\r\n<span style=\"color:grey; opacity: 0.6\">( Image credit: [Self-Supervised Semi-Supervised Learning](https://arxiv.org/pdf/1905.03670v2.pdf) )</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":["Accuracy"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Accuracy":"higher"}},"counts":{"rows":3,"rows_with_code":3,"rows_with_paper_page":3,"rows_dated":3,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"FeatMatch","metrics":{"Accuracy":"65.21"},"uses_additional_data":false,"paper_date":"2020-07-16","paper":"/paper/featmatch-feature-based-augmentation-for-semi","paper_url":"https://arxiv.org/abs/2007.08505v1","paper_title":"FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning","code":"https://github.com/GT-RIPL/FeatMatch","n_code_links":2,"syntology":{"n_ran":1,"n_unverified":0,"n_samples":1,"n_pointer_only_licence":1}},{"rank_in_archive_order":2,"model":"SemCo (μ=3)","metrics":{"Accuracy":"58.75±0.76"},"uses_additional_data":false,"paper_date":"2021-04-12","paper":"/paper/all-labels-are-not-created-equal-enhancing","paper_url":"https://arxiv.org/abs/2104.05248v1","paper_title":"All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-training","code":"https://github.com/islam-nassar/semco","n_code_links":1,"syntology":null},{"rank_in_archive_order":3,"model":"SemCo (μ=7)","metrics":{"Accuracy":"57.22±0.35"},"uses_additional_data":false,"paper_date":"2021-04-12","paper":"/paper/all-labels-are-not-created-equal-enhancing","paper_url":"https://arxiv.org/abs/2104.05248v1","paper_title":"All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-training","code":"https://github.com/islam-nassar/semco","n_code_links":1,"syntology":null}],"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":1,"rows_with_any_sample_ran":1,"distinct_papers_with_graph_line":1,"distinct_papers_with_any_sample_ran":1,"samples_over_distinct_papers":{"n_ran":1,"n_unverified":0,"n_samples":1,"n_pointer_only_licence":1,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":1,"n_unverified":0,"n_samples":1,"n_pointer_only_licence":1,"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"}}}