Summary of Explainable Ai Approach Using Near Misses Analysis, by Eran Kaufman and Avivit Levy
Explainable AI Approach using Near Misses Analysis
by Eran Kaufman, Avivit levy
First submitted to arxiv on: 25 Nov 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 The proposed novel XAI approach uses near-misses analysis (NMA) to reveal a hierarchy of logical ‘concepts’ inferred from the latent decision-making process of Neural Networks (NNs) without delving into their explicit structure. This method was tested on various network architectures and datasets, including ResNet, VGG, EfficientNet, MobileNet on ImageNet and CIFAR100. The results demonstrate the usability of this approach in reflecting NNs’ latent process of concept generation. Additionally, a new metric for explainability is introduced. The study also suggests that efficient architectures, which achieve similar accuracy with fewer neurons, may still compromise explainability and robustness. This research paves the way for future XAI research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to understand how Neural Networks think. It shows that by looking at what the network does when it’s close to making a mistake, we can figure out what concepts it uses to make decisions. The researchers tested this approach on different types of networks and datasets. They found that their method helps us see how networks are really working, even if they’re not very big or complex. This is important because it means we can start to understand why some networks are better at certain tasks than others. It’s like being able to read the network’s mind! |
Keywords
» Artificial intelligence » Resnet
