A Novel Attention-Based Multi-Modal Modeling Technique on Mixed Type Data for Improving TFT-LCD Repair Process

A Novel Attention-Based Multi-Modal Modeling Technique on Mixed Type Data for Improving TFT-LCD Repair Process

In Thin-Film Transistor Liquid-Crystal Display (TFT-LCD) manufacturing, conducting a machine learning based system with multiple data types has become actively desired to solve complicated problems. This paper proposes a multi-modal learning approach: <italic>TabVisionNet</italic>, which...

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Bibliografiska uppgifter
Huvudupphovsmän: Yi Liu, Hsueh-Ping Lu, Ching-Hao Lai
Materialtyp: Artikel
Språk:English
Publicerad: IEEE 2022-01-01
Serie:IEEE Access
Ämnen:
Smart manufacturing
TFT-LCD
deep learning
multi-modality machine learning
tabular data
image data and attention mechanism
Länkar:https://ieeexplore.ieee.org/document/9733354/

Internet

https://ieeexplore.ieee.org/document/9733354/

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