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Customizable and Programmable Deep Learning

Permanent lenke
https://hdl.handle.net/10037/36964
DOI
https://doi.org/10.1007/978-3-031-78107-0_7
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Åpne
article.pdf (711.8Kb)
Akseptert manusversjon (PDF)
Dato
2024-12-02
Type
Conference object
Konferansebidrag

Forfatter
Pal, Ratnabali; Sekh, Arif Ahmed
Sammendrag
In this study, we explore the potential of pre-trained deep learning models, proposing a new approach that emphasizes their reusability and adaptability. Our framework, termed “customizable” deep learning, facilities users to seamlessly integrate diverse pre-trained models for addressing new tasks and enhancing existing solutions. Furthermore, we introduce a “programmable” adapter that enables the flexible combination of different pre-trained modules, expanding the range of applications and customization options. Through empirical experiments, particularly focusing on Visual Question Answering (VQA) for visually impaired (VI) individuals, we demonstrate the practical effectiveness of our methodology. These contributions advance the deep learning field while promoting customization and re-usability across various domains and tasks. The code is available https://github.com/Ratnabali-Pal/CPDA-VQA.
Forlag
Springer Nature
Serie
Lecture Notes in Computer Science (LNCS) ; null
Sitering
Pal, R., Kar, S., Sekh, A.A. (2025). Customizable and Programmable Deep Learning. In: Antonacopoulos, A., Chaudhuri, S., Chellappa, R., Liu, CL., Bhattacharya, S., Pal, U. (eds) Pattern Recognition. ICPR 2024. Lecture Notes in Computer Science, vol 15301. Springer, Cham. https://doi.org/10.1007/978-3-031-78107-0_7
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