The proposed feedback-guided synthetic data generation framework can be adapted to address imbalanced datasets characterized by long-tailed distributions in the feature space by modifying the sampling strategy to focus on the feature characteristics rather than solely on class labels. This can be achieved through the following steps:
Feature Space Analysis: Begin by analyzing the feature distributions of the dataset to identify regions of the feature space that are underrepresented. This involves clustering techniques or density estimation methods to understand the distribution of features across different classes.
Feedback Mechanism: Extend the feedback mechanism to incorporate not just class labels but also feature representations. By utilizing a pre-trained model that captures the feature space dynamics, the framework can generate synthetic samples that are not only class-representative but also diverse in terms of feature characteristics.
Conditional Generation: Implement dual conditioning on both class labels and specific feature attributes. For instance, when generating synthetic samples, the framework can condition the generative model on a combination of class labels and specific feature vectors that represent the underrepresented regions in the feature space.
Diversity Enhancement: To ensure that the generated samples cover a broader range of the feature space, techniques such as random dropout on feature embeddings can be employed. This introduces variability in the generated samples, promoting diversity and reducing the risk of overfitting to specific feature patterns.
Evaluation Metrics: Adapt evaluation metrics to assess the performance of the model not only on classification accuracy but also on the coverage and density of the feature space. This ensures that the synthetic data generation process is effectively addressing the imbalances present in the feature space.
By implementing these strategies, the feedback-guided framework can effectively generate synthetic data that addresses imbalances in both the label and feature spaces, enhancing the robustness and generalization of downstream models.