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.

通过对单模型系统和多模型融合的研究，我们提出了一个基于对抗性互补表示学习框架，该框架使新训练的模型能够避免先前获取的知识，使每个单独的组成模型能够学习最大程度不同、互补的表示方法，并通过实验结果证明我们的方法相比传统的多模型融合能更有效地提高性能。此外，归因分析验证了在 ACoRL 下训练的模型获取了更多的互补知识，突显了我们的方法在提高效率和鲁棒性方面的有效性。