Mind the Gap: Bridging Prior Shift in Realistic Few-Shot Crop-Type Classification
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866911277393117184 |
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| author | Reuss, Joana Gikalo, Ekaterina Körner, Marco |
| author_facet | Reuss, Joana Gikalo, Ekaterina Körner, Marco |
| contents | Real-world agricultural distributions often suffer from severe class imbalance, typically following a long-tailed distribution. Labeled datasets for crop-type classification are inherently scarce and remain costly to obtain. When working with such limited data, training sets are frequently constructed to be artificially balanced -- in particular in the case of few-shot learning -- failing to reflect real-world conditions. This mismatch induces a shift between training and test label distributions, degrading real-world generalization. To address this, we propose Dirichlet Prior Augmentation (DirPA), a novel method that simulates an unknown label distribution skew of the target domain proactively during model training. Specifically, we model the real-world distribution as Dirichlet-distributed random variables, effectively performing a prior augmentation during few-shot learning. Our experiments show that DirPA successfully shifts the decision boundary and stabilizes the training process by acting as a dynamic feature regularizer. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_16218 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mind the Gap: Bridging Prior Shift in Realistic Few-Shot Crop-Type Classification Reuss, Joana Gikalo, Ekaterina Körner, Marco Machine Learning Real-world agricultural distributions often suffer from severe class imbalance, typically following a long-tailed distribution. Labeled datasets for crop-type classification are inherently scarce and remain costly to obtain. When working with such limited data, training sets are frequently constructed to be artificially balanced -- in particular in the case of few-shot learning -- failing to reflect real-world conditions. This mismatch induces a shift between training and test label distributions, degrading real-world generalization. To address this, we propose Dirichlet Prior Augmentation (DirPA), a novel method that simulates an unknown label distribution skew of the target domain proactively during model training. Specifically, we model the real-world distribution as Dirichlet-distributed random variables, effectively performing a prior augmentation during few-shot learning. Our experiments show that DirPA successfully shifts the decision boundary and stabilizes the training process by acting as a dynamic feature regularizer. |
| title | Mind the Gap: Bridging Prior Shift in Realistic Few-Shot Crop-Type Classification |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2511.16218 |