Summary of Towards Adversarial Robustness Of Model-level Mixture-of-experts Architectures For Semantic Segmentation, by Svetlana Pavlitska et al.
Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation
by Svetlana Pavlitska, Enrico Eisen, J. Marius Zöllner
First submitted to arxiv on: 16 Dec 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 This paper investigates the adversarial robustness of Mixture of Experts (MoE) models for semantic segmentation tasks. MoEs are a type of neural network that combines the outputs of multiple expert models using a learnable gating component. The authors evaluate the vulnerability of MoEs to various types of adversarial attacks, including white-box and transfer attacks. They find that MoEs are generally more robust than individual expert models and can even outperform ensembles on certain tasks. The authors also show that MoEs are less susceptible to per-instance and universal white-box attacks compared to traditional neural networks. This study provides insights into the adversarial vulnerability of MoE models, which is crucial for their application in real-world scenarios like semantic segmentation of traffic scenes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well Mixture of Experts (MoE) computer models can resist attacks that try to trick them. MoEs are special kinds of AI models that combine the predictions from multiple smaller models using a learnable “gating” component. The researchers tested how well MoEs perform against different types of attacks, including those designed specifically for this type of model. They found that MoEs are generally more resistant to these attacks than individual models and can even do better than combining many models together (called an ensemble). This study helps us understand how well MoEs work in real-world scenarios like recognizing traffic scenes. |
Keywords
» Artificial intelligence » Mixture of experts » Neural network » Semantic segmentation
