قارن الطرق

	المحوّل القابل للتفسير (Explainable Transformer)	المحولات متعددة الوسائط (Multimodal Transformers)
المجال	التعلم العميق	التعلم العميق
العائلة	Machine learning	Machine learning
سنة النشأة	2017–2021	2019–2021
صاحب الطريقة	Vaswani et al. (Transformer); explainability extensions by Chefer et al. and the broader XAI community	Lu et al. (ViLBERT); Radford et al. (CLIP)
النوع	Interpretable deep learning model	Cross-modal attention-based deep learning model
المصدر التأسيسي	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.	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.
الأسماء البديلة	XAI Transformer, Interpretable Transformer, Transparent Transformer, Explainable Attention Model	multimodal attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer
ذات صلة	4	5
الملخص	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.