Summary of Demansia: Mamba Never Forgets Any Tokens, by Ricky Fang
DeMansia: Mamba Never Forgets Any Tokens
by Ricky Fang
First submitted to arxiv on: 4 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 A transformer architecture-based research paper examines the mathematical foundations of transformers, highlighting their limitations in handling long sequences. The study builds upon prerequisite models such as Mamba, Vision Mamba (ViM), and LV-ViT to propose a novel architecture, DeMansia. DeMansia integrates state space models with token labeling techniques to improve performance in image classification tasks, addressing computational challenges posed by traditional transformers. The paper demonstrates the effectiveness of DeMansia through benchmarking and comparisons with contemporary models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how transformer architectures work mathematically and finds that they have limitations when dealing with long sequences. It uses earlier models like Mamba, Vision Mamba (ViM), and LV-ViT as a starting point to create a new model called DeMansia. DeMansia combines state space models with token labeling techniques to make it better at image classification tasks, which helps solve some computational problems. The paper shows how well DeMansia works by comparing it to other models. |
Keywords
* Artificial intelligence * Image classification * Token * Transformer * Vit
