เปรียบเทียบวิธี

	Explainable Multilayer Perceptron	ทรานส์ฟอร์มเมอร์ที่อธิบายได้
สาขาวิชา	การเรียนรู้เชิงลึก	การเรียนรู้เชิงลึก
ตระกูล	Machine learning	Machine learning
ปีกำเนิด	2010s–present	2017–2021
ผู้ริเริ่ม	Lundberg & Lee (SHAP); Ribeiro et al. (LIME); broader XAI community	Vaswani et al. (Transformer); explainability extensions by Chefer et al. and the broader XAI community
ประเภท	Supervised feedforward neural network with interpretability layer	Interpretable deep learning model
แหล่งต้นตำรับ	Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.	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.
ชื่อเรียกอื่น	XMLP, Interpretable MLP, Explainable feedforward neural network, Transparent MLP	XAI Transformer, Interpretable Transformer, Transparent Transformer, Explainable Attention Model
ที่เกี่ยวข้อง	4	4
สรุป	An Explainable Multilayer Perceptron (XMLP) is a standard feedforward neural network trained with backpropagation, augmented with post-hoc interpretability techniques — such as SHAP values, LIME, or integrated gradients — that attribute each prediction to individual input features. The combination retains the MLP's approximation power while satisfying transparency requirements common in regulated or high-stakes domains.	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.