Open-World Semi-Supervised Learning

Benchmarks archive 2025-07-28

3 leaderboard tables shown for this task, 3 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

4 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

13 shown of 13 papers with code (17 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Parametric Classification for Generalized Category Discovery: A Baseline Study
  21 Nov 2022 2 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  Generalized Category Discovery (GCD) aims to discover novel categories in unlabelled datasets using knowledge learned from labelled samples.
• OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning
  4 Nov 2024 1 repository listed Syntology ran 5 of 13 samples · 8 unverified
  However, the emergence of open-world SSL (OwSSL) introduces a more practical challenge, wherein unlabeled data may encompass samples from unseen classes.
• Open-World Semi-Supervised Learning for Node Classification
  18 Mar 2024 1 repository listed
  Open-world semi-supervised learning (Open-world SSL) for node classification, that classifies unlabeled nodes into seen classes or multiple novel classes, is a practical but under-explored problem in the graph community.
• Robust Semi-Supervised Learning for Self-learning Open-World Classes
  15 Jan 2024 1 repository listed
  Existing semi-supervised learning (SSL) methods assume that labeled and unlabeled data share the same class space.
• Targeted Representation Alignment for Open-World Semi-Supervised Learning
  1 Jan 2024 1 repository listed
  Open-world Semi-Supervised Learning aims to classify unlabeled samples utilizing information from labeled data while unlabeled samples are not only from the labeled known categories but also from novel categories…
• A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning
  6 Nov 2023 1 repository listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  Open-world semi-supervised learning aims at inferring both known and novel classes in unlabeled data, by harnessing prior knowledge from a labeled set with known classes.
• Bridging the Gap: Learning Pace Synchronization for Open-World Semi-Supervised Learning
  21 Sep 2023 1 repository listed
  In open-world semi-supervised learning, a machine learning model is tasked with uncovering novel categories from unlabeled data while maintaining performance on seen categories from labeled data.
• Discover and Align Taxonomic Context Priors for Open-world Semi-Supervised Learning
  21 Sep 2023 1 repository listed
  It allows us to discover multi-granularity semantic concepts as taxonomic context priors (i.
• Discover and Align Taxonomic Context Priors for Open-world Semi-Supervised Learning
  21 Sep 2023 1 repository listed
  It allows us to discover multi-granularity semantic concepts as taxonomic context priors (i.
• Towards Realistic Semi-Supervised Learning
  5 Jul 2022 1 repository listed Syntology ran 2 of 2 samples · 0 unverified
  We also highlight the flexibility of our approach in solving novel class discovery task, demonstrate its stability in dealing with imbalanced data, and complement our approach with a technique to estimate the number of…
• OpenLDN: Learning to Discover Novel Classes for Open-World Semi-Supervised Learning
  5 Jul 2022 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  In the open-world SSL problem, the objective is to recognize samples of known classes, and simultaneously detect and cluster samples belonging to novel classes present in unlabeled data.
• Generalized Category Discovery
  7 Jan 2022 1 repository listed Syntology ran 5 of 11 samples · 6 unverified
  Here, the unlabelled images may come from labelled classes or from novel ones.
• Open-World Semi-Supervised Learning
  6 Feb 2021 1 repository listed
  Here, we introduce a novel open-world semi-supervised learning setting that formalizes the notion that novel classes may appear in the unlabeled test data.

Syntology lines on 6 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.