We lifted 3 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| selinakhan/stylistic-MTL-ukiyoe | canonical | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| accuracy_MTL | Ran | selinakhan/stylistic-MTL-ukiyoe/evaluate.py pointer only (licence: NONE) · get_code("a419cd45f419d2b6") |
| accuracy_MTL_supp | Ran | selinakhan/stylistic-MTL-ukiyoe/evaluate.py pointer only (licence: NONE) · get_code("88dfac20743063bf") |
| accuracy_STL_regression | Ran | selinakhan/stylistic-MTL-ukiyoe/evaluate.py pointer only (licence: NONE) · get_code("0f8b431f35aad0e4") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In this work we present a large-scale dataset of \textit{Ukiyo-e} woodblock prints. Unlike previous works and datasets in the artistic domain that primarily focus on western art, this paper explores this pre-modern Japanese art form with the aim of broadening the scope for stylistic analysis and to provide a benchmark to evaluate a variety of art focused Computer Vision approaches. Our dataset consists of over $175.000$ prints with corresponding metadata (\eg artist, era, and creation date) from the 17th century to present day. By approaching stylistic analysis as a Multi-Task problem we aim to more efficiently utilize the available metadata, and learn more general representations of style. We show results for well-known baselines and state-of-the-art multi-task learning frameworks to enable future comparison, and to encourage stylistic analysis on this artistic domain.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2410.12379")
get_code_for_paper("2410.12379")
have("2410.12379")
Connect an agent — have() is free.