Browse State-of-the-Art › Photo to Rest Generalization
Photo to Rest Generalization
3 papers with code · 2 benchmarks · 1 dataset archive 2025-07-28
It is the practical scenario of training on set of easy-to-collect real photographs and evaluate on the rest of diverse-styled domains (art, cartoon, sketch). Photo-to-rest generalization is a special case of the single-source domain generalization (SSDG) task.
Using only real photographs for training is the only way for SSDG to be compatible with ImageNet pre-trained networks. For any other domain as source, access to the photo domain through ImageNet pre-training violates the hypothesis of the task of a single domain seen during training, taking the task closer to the multy-source domain generalization (MSDG). Finaly pre-training on imagenet, training on a non real photograph domain and testing on real photographs violates the SSDG hypothesis that the target domain should not be used during training.
A large part of the generalization community chooses only this task over SSDG for pre-trained networks and for datasets that contain the photo domain.
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
2 leaderboard tables shown for this task, 2 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.
| Dataset | Best model (first row in archive order) | Paper | Code | Syntology | Compare |
|---|---|---|---|---|---|
| PACS (8 rows) | Crafting-Shifts(ResNet18) | Crafting Distribution Shifts for Validation and Training in Single... | code | — | Compare |
| MiniDomainNet (1 row) | Crafting-Shifts(ResNet18) | Crafting Distribution Shifts for Validation and Training in Single... | code | — | Compare |
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.
Parent tasks archive 2025-07-28
Most implemented papers archive 2025-07-28
3 shown of 3 papers with code (7 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.
-
29 Sep 2024 1 repository listedThe method that achieves the best performance on the augmented validation is selected from the proposed family.
-
18 Jul 2023 1 repository listed Syntology ran 4 of 5 samples · 1 unverified · 5 pointer-only (licence)Generalizing to unseen image domains is a challenging problem primarily due to the lack of diverse training data, inaccessible target data, and the large domain shift that may exist in many real-world settings.
-
26 Aug 2021 1 repository listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)Domain generalization (DG) aims to generalize a model trained on multiple source (i.
Syntology lines on 2 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.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections