How can the SED framework be extended to other types of diversification objectives beyond the "Agree to Disagree" (A2D) method used in this work?
The Scalable Ensemble Diversification (SED) framework can be extended to incorporate various diversification objectives by integrating different regularization techniques that promote model disagreement or diversity in predictions. For instance, one could explore the use of feature-space diversification, where the focus shifts from output predictions to the diversity of learned features across the ensemble members. This could involve regularizing the distance between the feature representations of different models, encouraging them to learn distinct representations of the input data.
Another potential extension is to implement input perturbation strategies, where the models are trained on augmented versions of the input data. This could involve applying different types of noise or transformations to the input samples, thereby fostering diversity in the model responses. Additionally, the SED framework could leverage ensemble distillation techniques, where a teacher model (an ensemble) guides the training of student models, promoting diversity through the distillation process.
Moreover, the SED framework could be adapted to include multi-objective optimization, where multiple diversification objectives are simultaneously optimized. This could involve balancing between prediction disagreement, feature diversity, and robustness to adversarial examples, thereby enhancing the overall performance of the ensemble in various tasks.