{"url":"/sota/semi-supervised-image-classification-cold","task":{"name":"Semi-Supervised Image Classification (Cold Start)","url":"/task/semi-supervised-image-classification-cold","note":null},"dataset":{"name":"CIFAR-10, 30 Labels","url":"/dataset/cifar-10"},"category":"Computer Vision","categories":["Computer Vision"],"category_note":null,"description":"This is the same as the [semi-supervised image classification task](https://paperswithcode.com/task/semi-supervised-image-classification), with the key difference being that the labelled subset chosen needs to be selection in a class agnostic manner. This means that the standard practice in semi-supervised learning of using a random class stratified sample is \"cheating\" in this case, as class information is required for the whole dataset for this to be done. Rather, this challenge requires a smart cold-start or unsupervised selective labelling strategy to identify images that are most informative and result in the best performing models.","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":["Percentage error"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Percentage error":"lower"}},"counts":{"rows":1,"rows_with_code":1,"rows_with_paper_page":1,"rows_dated":1,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"SimCLR-kmediods-PAWS","metrics":{"Percentage error":"6.4"},"uses_additional_data":false,"paper_date":"2023-05-17","paper":"/paper/cold-paws-unsupervised-class-discovery-and-1","paper_url":"https://arxiv.org/abs/2305.10071v2","paper_title":"Cold PAWS: Unsupervised class discovery and addressing the cold-start problem for semi-supervised learning","code":"https://github.com/emannix/cold-paws-simclr-and-paws-semi-supervised-learning","n_code_links":2,"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":0,"rows_with_any_sample_ran":0,"distinct_papers_with_graph_line":0,"distinct_papers_with_any_sample_ran":0,"samples_over_distinct_papers":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"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"}}}