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Achieve consistently high accuracy across all animal behaviors by adaptively customizing features and calibrating the classifier to overcome suboptimal sampling rates and class imbalance.
Personalized federated learning with client-specific batch normalization can dramatically outperform generic approaches in medical image segmentation, but the best method is highly dataset-dependent.
