Summary of Few-shot Metric Domain Adaptation: Practical Learning Strategies For An Automated Plant Disease Diagnosis, by Shoma Kudo et al.
Few-shot Metric Domain Adaptation: Practical Learning Strategies for an Automated Plant Disease Diagnosis
by Shoma Kudo, Satoshi Kagiwada, Hitoshi Iyatomi
First submitted to arxiv on: 25 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 The proposed Few-shot Metric Domain Adaptation (FMDA) approach enhances diagnostic accuracy in practical image-based automated systems for plant disease diagnosis by reducing domain discrepancies and minimizing the distance between feature spaces of source and target data with limited samples. This flexible and computationally efficient method can be seamlessly integrated into any machine learning pipeline, achieving F1 score improvements of 11.1 to 29.3 points compared to cases without target data using only 10 images per disease from the target domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FMDA is a new way to help machines better diagnose plant diseases by looking at pictures. Right now, these systems are really good but they can be tricked if the pictures look different from what they were trained on. FMDA makes the system more flexible so it can do well even when it only has a few new pictures to look at. This is important because we want machines to help farmers quickly and accurately diagnose diseases. |
Keywords
» Artificial intelligence » Domain adaptation » F1 score » Few shot » Machine learning
