Adversarial complementary learning for just noticeable difference estimation
Recently, many unsupervised learning-based models have emerged for Just Noticeable Difference (JND) estimation, demonstrating remarkable improvements in accuracy. However, these models suffer from a significant drawback is that their heavy reliance on handcrafted priors for guidance. This restricts...
| Päätekijät: | , , , , |
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| Muut tekijät: | |
| Aineistotyyppi: | Journal Article |
| Kieli: | English |
| Julkaistu: |
2024
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| Aiheet: | |
| Linkit: | https://hdl.handle.net/10356/178981 |