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| 설명 가능한 인스턴스 분할× | 설명 가능한 비전 트랜스포머(Explainable Vision Transformer)× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2017–present | 2021 |
| 창시자≠ | He, K. et al. (Mask R-CNN); XAI extensions by multiple authors | Chefer, H., Gur, S., & Wolf, L. (attribution framework); Dosovitskiy et al. (base ViT) |
| 유형≠ | Explainability-augmented deep learning pipeline | Post-hoc explainability applied to Vision Transformer |
| 원전≠ | Lindner, M., Meng, C., & Bischl, B. (2023). Explaining Instance Segmentation Models via Saliency Maps and Occlusion. IEEE Transactions on Pattern Analysis and Machine Intelligence. link ↗ | Chefer, H., Gur, S., & Wolf, L. (2021). Transformer interpretability beyond attention visualization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 782–791. DOI ↗ |
| 별칭 | XAI instance segmentation, interpretable instance segmentation, transparent mask prediction, explainable Mask R-CNN | XViT, Interpretable ViT, Explainable ViT, Transparent Vision Transformer |
| 관련≠ | 6 | 5 |
| 요약≠ | Explainable Instance Segmentation combines deep-learning instance segmentation models — which detect and delineate every individual object as a separate pixel mask — with post-hoc or ante-hoc explainability techniques such as GradCAM, SHAP, LIME, or attention visualization, so that each predicted mask is accompanied by evidence showing which image regions drove the model's decision. | Explainable Vision Transformer combines the strong image-recognition performance of Vision Transformers (ViT) with attribution techniques — such as relevance propagation, attention rollout, or gradient-weighted attention — that highlight which image regions drive each prediction. The approach enables researchers and practitioners to audit model decisions and satisfy transparency requirements without sacrificing accuracy. |
| ScholarGate데이터셋 ↗ |
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