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DeConvolve: Towards Textually Explainable and Human Cognizable Convolutional Networks Cover

DeConvolve: Towards Textually Explainable and Human Cognizable Convolutional Networks

Cybernetics and Information Technologies
Volume 25 (2025): Issue 3 (September 2025)
By: Meeradevi,  Hrishikesh Haritas,  Darshan Bankapure and  Divyansh Mishra  
Open Access
|Sep 2025

Authors

Meeradevi

meera_ak@msrit.edu

Department of Artificial Intelligence and Machine Learning, Ramaiah Institute of Technology, Bengaluru, India

Hrishikesh Haritas

1ms21ai022@msrit.edu

Department of Artificial Intelligence and Machine Learning, Ramaiah Institute of Technology, Bengaluru, India

Darshan Bankapure

1ms21ai016@msrit.edu

Department of Artificial Intelligence and Machine Learning, Ramaiah Institute of Technology, Bengaluru, India

Divyansh Mishra

1ms21ad022@msrit.edu

Department of Artificial Intelligence and Data Science, Ramaiah Institute of Technology, Bengaluru, India
DOI: https://doi.org/10.2478/cait-2025-0020 | Journal eISSN: 1314-4081 | Journal ISSN: 1311-9702
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Language: English
Page range: 22 - 38
Submitted on: Apr 14, 2025
|
Accepted on: Jun 30, 2025
|
Published on: Sep 25, 2025
Published by: Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Convolutional neural networks,
Contrastive language-image pretraining,
Gradient-weighted class activation mapping,
Human recognizable,
Large language models
Related subjects:
Computer sciences,
Information technology

© 2025 Meeradevi, Hrishikesh Haritas, Darshan Bankapure, Divyansh Mishra, published by Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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