Enhancing Multi-Model Fusion Performance through Adversarial Complementary Representation Learning

The proposed adversarial complementary representation learning (ACoRL) framework enables newly trained models to avoid previously acquired knowledge, allowing each individual component model to learn maximally distinct, complementary representations, which improves the efficiency and robustness of multi-model fusion.