Historical Color Image Dating

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 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

1 dataset 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.

Most implemented papers archive 2025-07-28

4 shown of 4 papers with code (6 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.

• OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression
  6 Jun 2022 1 repository listed
  In this paper, we propose to learn the rank concepts from the rich semantic CLIP latent space.
• Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware Regression
  25 Mar 2021 1 repository listed Syntology ran 8 of 8 samples · 0 unverified · 8 pointer-only (licence)
  An ordinal distribution constraint is proposed to exploit the ordinal nature of regression.
• Deep Ordinal Regression with Label Diversity
  29 Jun 2020 1 repository listed
  By discretizing the target into a set of non-overlapping classes, it has been shown that training a classifier can improve neural network accuracy compared to using a standard regression approach.
• Non-parametric Uni-modality Constraints for Deep Ordinal Classification
  25 Nov 2019 1 repository listed
  We propose a new constrained-optimization formulation for deep ordinal classification, in which uni-modality of the label distribution is enforced implicitly via a set of inequality constraints over all the pairs of…

Syntology lines on 1 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.