In the context of limited training data, ensuring the robustness of the automatic selection of the number of prototypes per class is crucial. Several approaches can be adopted to address this challenge:
Data Augmentation: Leveraging data augmentation techniques can help increase the effective size of the training data, enabling more reliable estimation of the number of prototypes. By generating augmented samples, the diversity and representativeness of the data can be enhanced.
Transfer Learning: Utilizing pre-trained models or knowledge from related tasks can provide additional information for determining the optimal number of prototypes. Transfer learning can help mitigate the effects of limited training data and improve the robustness of the selection process.
Ensemble Methods: Employing ensemble methods to combine multiple prototype selection strategies can enhance the stability and reliability of the selection process. By aggregating the results from different approaches, the risk of overfitting to the limited data can be reduced.
Regularization Techniques: Introducing regularization techniques, such as L1 or L2 regularization, can prevent overfitting and promote generalization in the selection of prototypes. Regularization helps control the complexity of the model and improves its performance on unseen data.
Cross-Validation: Implementing cross-validation procedures can validate the selection of prototypes and assess their performance across different subsets of the limited training data. Cross-validation ensures the robustness and generalizability of the selected prototypes.
By integrating these strategies, the automatic selection of the number of prototypes per class can be made more robust, even in scenarios with limited training data, enhancing the reliability and effectiveness of the prototypical explanations.