  Single-model systems often suffer from deficiencies in tasks such as speaker
verification (SV) and image classification, relying heavily on partial prior
knowledge during decision-making, resulting in suboptimal performance. Although
multi-model fusion (MMF) can mitigate some of these issues, redundancy in
learned representations may limits improvements. To this end, we propose an
adversarial complementary representation learning (ACoRL) framework that
enables newly trained models to avoid previously acquired knowledge, allowing
each individual component model to learn maximally distinct, complementary
representations. We make three detailed explanations of why this works and
experimental results demonstrate that our method more efficiently improves
performance compared to traditional MMF. Furthermore, attribution analysis
validates the model trained under ACoRL acquires more complementary knowledge,
highlighting the efficacy of our approach in enhancing efficiency and
robustness across tasks.
