Summary of Ninformer: a Network in Network Transformer with Token Mixing As a Gating Function Generator, by Abdullah Nazhat Abdullah et al.
NiNformer: A Network in Network Transformer with Token Mixing as a Gating Function Generator
by Abdullah Nazhat Abdullah, Tarkan Aydin
First submitted to arxiv on: 4 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 The paper introduces a new computational block to reduce the computational burden and alleviate data size requirements in the Vision Transformer (ViT) architecture, which has led to significant advancements in computer vision and other domains. The proposed design replaces traditional attention layers with a Network-in-Network structure that learns an element-wise gating function through token mixing, offering improved performance on multiple image classification datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making the Vision Transformer (ViT) architecture better by reducing its computational costs and needing less data to work effectively. The current ViT is very powerful but also uses a lot of computing power and requires large amounts of data. To fix this, many people have proposed new ideas to make it more efficient. This paper introduces a new way to do this, which replaces the traditional attention layers with a different approach that learns how to focus on important parts of the image. The results show that this new design performs better than other versions on several image classification tasks. |
Keywords
* Artificial intelligence * Attention * Image classification * Token * Vision transformer * Vit
