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Original Article
Deep Meme Automated Image Text Meme Production Via Convolutional Neural Networks
Moni Chaurasiya1
Sanchi Deshmukh2
Kaikashan Siddavatam3
Sarita Kori4
Nikki Shukla5
1245University of Mumbai, Maharashtra, India. 3 Professor, University of Mumbai, Maharashtra, India.
Published Online: March-April 2025
Pages: 27-33
Cite this article
No DOIReferences
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International Conference on Advances in Social Networks Analysis and Mining (ASO.AM 2013). IEEE; 2013:548-555.
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[Accessed 21 Mar. 2018].
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networks. arXiv preprint arXiv:1806.04510
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descriptions. In Proceedings of the IEEE conference on com puter vision and pattern recognition, pages 3128–3137
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the North Amer ican Chapter of the Association for Computa tional Linguistics: Human Language Technolo gies, pages 355–365.
16. QuickMeme. 2016. Quick Meme Website. http://quickmeme.com/.
2. Florian Colombo, Alexander Seeholzer, and Wulfram Gerstner. 2017. Deep artificial composer: A creative neural network model for
automated melody generation. In International Conference on Evolutionary and Biologically Inspired Music and Art. 81–96.
3. Shifman L. Memes in a digital world: Reconciling with a conceptual troublemaker. Journal of computer mediated communication.
2013;18(3):362-377. doi:10.1 111/jcc4.12013
4. Gal N, Shifman L, Kampf Z. “it gets better”: Internet memes and the construction of collective identity. New media & society.
2016;18(8):1698-1714,.
5. O. Vinyals, A. Toshev, S. Bengio, and D. Erhan, Show and tell: A neural image caption generator, 2015 IEEE Conference on Computer
Vision and Pattern Recognition (CVPR), 2015.
6. Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. ImageNet classification with deep con volutional neural networks.
Proceedings of the 25th International Conference on Neural Information Pro cessing Systems- Volume 1 (NIPS’12)
7. Xu, K. et al. Show, attend and tell: Neural image caption generation with visual attention. Proc. Interna tional Conference on Learning
Representations (2015).
8. Mun, J., Cho, M., Han, B.: Text-guided attention model for image captioning. AAAI (2016).
9. Ferrara E, JafariAsbagh M, Varol O, Qazvinian V, Menczer F, Flammini A. Clustering memes in social media. In: 2013 IEEE/ACM
International Conference on Advances in Social Networks Analysis and Mining (ASO.AM 2013). IEEE; 2013:548-555.
doi:10.1145/249 2517.2492530
10. Merriam-webster.com. (2018). Definition of MEME. [online] Available at: https://www.merriam webster.com/dictionary/meme
[Accessed 21 Mar. 2018].
11. GitHub. (2018). tensorflow/models. [online] Available at: https://github.com/tensorflow/models/tree /master/research/im2txt#model-
overview [Accessed 21 Mar. 2018].
12. Tensorflow authors, Official ‘Show and Tell: A neural image caption generator model’ implementation,
https://github.com/tensorflow/models
13. Peirson V and Tolunay2018. Abel L Peirson V and E Meltem Tolunay. 2018. Dank learning: Generating memes using deep neural
networks. arXiv preprint arXiv:1806.04510
14. Karpathy and Fei-Fei2015. Andrej Karpathy and Li Fei-Fei. 2015. Deep visual-semantic align ments for generating image
descriptions. In Proceedings of the IEEE conference on com puter vision and pattern recognition, pages 3128–3137
15. Wang and Wen2015. William Yang Wang and Miaomiao Wen. 2015. I can has cheezburger? a nonparanormal approach to combining
textual and visual information for predicting and gener ating popular meme descriptions. In Proceed ings of the 2015 Conference of
the North Amer ican Chapter of the Association for Computa tional Linguistics: Human Language Technolo gies, pages 355–365.
16. QuickMeme. 2016. Quick Meme Website. http://quickmeme.com/.
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