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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.20013 |
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| _version_ | 1866909274139000832 |
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| author | Vetter, Dennis Ahsan, Muhammad Delicado, Diana Neubauer, Thomas A. Wilke, Thomas Roig, Gemma |
| author_facet | Vetter, Dennis Ahsan, Muhammad Delicado, Diana Neubauer, Thomas A. Wilke, Thomas Roig, Gemma |
| contents | In this paper, we present our first proposal of a machine learning system for the classification of freshwater snails of the genus Radomaniola. We elaborate on the specific challenges encountered during system design, and how we tackled them; namely a small, very imbalanced dataset with a high number of classes and high visual similarity between classes. We then show how we employed triplet networks and the multiple input modalities of images, measurements, and genetic information to overcome these challenges and reach a performance comparable to that of a trained domain expert. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_20013 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Classification of freshwater snails of the genus Radomaniola with multimodal triplet networks Vetter, Dennis Ahsan, Muhammad Delicado, Diana Neubauer, Thomas A. Wilke, Thomas Roig, Gemma Computer Vision and Pattern Recognition Machine Learning In this paper, we present our first proposal of a machine learning system for the classification of freshwater snails of the genus Radomaniola. We elaborate on the specific challenges encountered during system design, and how we tackled them; namely a small, very imbalanced dataset with a high number of classes and high visual similarity between classes. We then show how we employed triplet networks and the multiple input modalities of images, measurements, and genetic information to overcome these challenges and reach a performance comparable to that of a trained domain expert. |
| title | Classification of freshwater snails of the genus Radomaniola with multimodal triplet networks |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2407.20013 |