Ugawaji wa Kisemantiki Unaoweza Kufafanuliwa
Ugawaji wa Kisemantiki Unaoweza Kufafanuliwa (XSS) huunganisha uchanganuzi wa eneo wa pikseli-kwa-pikseli — kukabidhi lebo ya darasa kwa kila pikseli kwenye picha — na mbinu za ufafanuzi za baada ya tukio au za ndani kama vile Grad-CAM, ramani za umakini, au SHAP, ili maamuzi ya darasa ya mtandao yaweze kukaguliwa, kuonekana, na kuhalalishwa kwa wataalamu wa fani katika upigaji picha wa kimatibabu, uendeshaji wa magari bila dereva, na hisia za mbali.
Soma mbinu kamili
Ingia kwa akaunti ya bure ili kusoma sehemu hii.
Method map
The neighbourhood of related methods — select a node to explore.
Vyanzo
- Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-CAM: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 618–626. DOI: 10.1109/ICCV.2017.74 ↗
- Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3431–3440. DOI: 10.1109/CVPR.2015.7298965 ↗
Jinsi ya kunukuu ukurasa huu
ScholarGate. (2026, June 3). Explainable Semantic Segmentation (XAI-Integrated Pixel-Wise Scene Parsing). ScholarGate. https://scholargate.app/sw/deep-learning/explainable-semantic-segmentation
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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