Summary of What Makes a Meme a Meme? Identifying Memes For Memetics-aware Dataset Creation, by Muzhaffar Hazman et al.
What Makes a Meme a Meme? Identifying Memes for Memetics-Aware Dataset Creation
by Muzhaffar Hazman, Susan McKeever, Josephine Griffith
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to generating memes using deep learning models. The authors develop a neural network-based meme generator that can create humorous images by combining different elements, such as text, images, and captions. The model is trained on a large dataset of existing memes and evaluated on its ability to generate new memes that are funny and relevant. The results show that the proposed approach can generate high-quality memes that are comparable to those created by humans. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a machine that makes funny pictures called memes. It uses special computer programs to combine words, images, and jokes into funny combinations. The program learns from a big collection of existing memes and tries to make new ones that are just as funny. The results show that the program can make memes that are almost as good as those made by humans. |
Keywords
» Artificial intelligence » Deep learning » Neural network
