The multi-exemplar pairing strategy proposed in the DELTA framework is a promising approach to enhance the robustness and generalization of the continual learner. To further improve this strategy and strike a better balance between stability and plasticity, several enhancements can be considered:
Dynamic Exemplar Selection: Implement a dynamic selection mechanism for exemplars based on their relevance to the current task. This can involve prioritizing exemplars that are more representative of the current data distribution or those that have been less frequently encountered.
Adaptive Pairing: Introduce an adaptive pairing mechanism that adjusts the number of exemplars paired with each input sample based on the complexity of the task or the model's learning progress. This adaptive approach can help maintain a balance between stability and plasticity.
Regularization Techniques: Incorporate regularization techniques, such as dropout or weight decay, specifically tailored for the multi-exemplar pairing scenario. These techniques can help prevent overfitting and promote better generalization.
Ensemble Learning: Explore ensemble learning methods where predictions from multiple paired exemplars are combined to make final decisions. This can help mitigate the impact of noisy or conflicting exemplars on the learning process.
Task-Specific Pairing Strategies: Develop task-specific pairing strategies that consider the unique characteristics of each task, such as the class distribution, sample complexity, or data variability. By tailoring the pairing strategy to each task, the model can adapt more effectively to different learning scenarios.
By incorporating these enhancements, the multi-exemplar pairing strategy can be further optimized to strike a better balance between stability and plasticity, leading to improved performance in long-tailed online continual learning scenarios.