Summary of Challenging the Black Box: a Comprehensive Evaluation Of Attribution Maps Of Cnn Applications in Agriculture and Forestry, by Lars Nieradzik et al.
Challenging the Black Box: A Comprehensive Evaluation of Attribution Maps of CNN Applications in Agriculture and Forestry
by Lars Nieradzik, Henrike Stephani, Jördis Sieburg-Rockel, Stephanie Helmling, Andrea Olbrich, Janis Keuper
First submitted to arxiv on: 18 Feb 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 explores the explainability of neural networks in agriculture and forestry, focusing on fertilizer treatment classification and wood identification. State-of-the-art Attribution Maps (AMs) are evaluated to understand their limitations and potential for uncovering critical features. The analysis reveals that AMs often misalign with expert-identified important features, raising concerns about their trustworthiness and practicality. This study provides insights into the decision-making process of neural networks in these application areas. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well AI can explain its decisions when used in farming and forestry. It tests special maps that help people understand why an AI made a certain choice, like classifying fertilizer or identifying wood. The results show that these maps don’t always work well and sometimes highlight the wrong features. This means we need to think carefully about whether these maps are reliable and useful for making decisions in these fields. |
Keywords
* Artificial intelligence * Classification
