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| 설명 가능한 트랜스포머× | 다중 모달 트랜스포머× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2017–2021 | 2019–2021 |
| 창시자≠ | Vaswani et al. (Transformer); explainability extensions by Chefer et al. and the broader XAI community | Lu et al. (ViLBERT); Radford et al. (CLIP) |
| 유형≠ | Interpretable deep learning model | Cross-modal attention-based deep learning model |
| 원전≠ | Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30. link ↗ | Lu, J., Batra, D., Parikh, D., & Lee, S. (2019). ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks. Advances in Neural Information Processing Systems (NeurIPS), 32. link ↗ |
| 별칭 | XAI Transformer, Interpretable Transformer, Transparent Transformer, Explainable Attention Model | multimodal attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer |
| 관련≠ | 4 | 5 |
| 요약≠ | An Explainable Transformer combines a standard or pre-trained Transformer architecture with post-hoc or built-in interpretability techniques — such as attention rollout, gradient-weighted attention, or SHAP — to reveal which input tokens or regions drove each prediction. The approach bridges high predictive accuracy with the transparency required in high-stakes or regulated domains. | A Multimodal Transformer extends the standard Transformer architecture to process and jointly reason over two or more input modalities — most commonly text and images, but also audio, video, or structured data. Cross-modal attention layers allow information from one modality to inform representations in another, enabling tasks such as visual question answering, image captioning, and multimodal sentiment analysis. |
| ScholarGate데이터셋 ↗ |
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