Research Article
Big Transfer Learning for Fine Art Classification
Table 2
State-of-the-art results for artist and style categorization on the Painting-91 dataset.
| Reference | Method | Painting-91 | Artist | Style | Average |
| [37] | LDCF (learned from gram) | 64.32 | 78.27 | 71.30 | [39] | Cross-layer correlation | 70.65 | 78.13 | 74.39 | [34] | Structure selection | 71.27 | 79.23 | 75.25 | BiT-S (ours) | Big transfer learning | 61.23 | 75.64 | 68.44 | BiT-M (ours) | Big transfer learning | 72.07 | 79.60 | 75.84 |
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The bold values show that the proposed BiT-M model achieves state-of-the-art performance compared with previous work and BiT-S model.
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