Summary of Mixture Of Experts in Image Classification: What’s the Sweet Spot?, by Mathurin Videau et al.
Mixture of Experts in Image Classification: What’s the Sweet Spot?
by Mathurin Videau, Alessandro Leite, Marc Schoenauer, Olivier Teytaud
First submitted to arxiv on: 27 Nov 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 investigates the integration of Mixture-of-Experts (MoE) models within computer vision frameworks, aiming to improve parameter efficiency and scalability. MoE layers are introduced in image classification models, and various configurations are tested on open datasets. The results show that moderate-sized MoE layers with a moderate number of activated parameters per sample lead to the best performance. However, this improvement diminishes as the number of parameters increases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how Mixture-of-Experts (MoE) models can be used in computer vision tasks like image classification. The researchers tested different settings for MoE layers and found that a moderate-sized layer with a certain amount of “expert” knowledge works best. This is good news because it means we might not need to use as many computing resources or collect as much data to get good results. |
Keywords
» Artificial intelligence » Image classification » Mixture of experts
