Σύγκριση μεθόδων

	Επεξηγήσιμος Μετασχηματιστής (Explainable Transformer)	Πολυτροπικός Μετασχηματιστής
Πεδίο	Βαθιά Μάθηση	Βαθιά Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2017–2021	2019–2021
Δημιουργός	Vaswani et al. (Transformer); explainability extensions by Chefer et al. and the broader XAI community	Lu et al. (ViLBERT); Radford et al. (CLIP)
Τύπος	Interpretable deep learning model	Cross-modal attention-based deep learning model
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	XAI Transformer, Interpretable Transformer, Transparent Transformer, Explainable Attention Model	multimodal attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer
Συναφείς	4	5
Σύνοψη	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.