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.

翻译：单模型系统在说话人验证（SV）和图像分类等任务中常存在缺陷，过度依赖决策过程中的部分先验知识，导致性能次优。尽管多模型融合（MMF）可缓解部分问题，但所学表示中的冗余可能限制性能提升。为此，我们提出对抗互补表示学习（ACoRL）框架，使新训练的模型能够避免先前已获取的知识，从而让每个独立组件模型学习到最大程度不同且互补的表示。我们从三个层面详细阐释该方法的有效性，实验结果表明，与传统MMF相比，本方法能更高效地提升性能。此外，归因分析验证了在ACoRL框架下训练的模型能获取更多互补知识，突显了本方法在提升各任务效率与鲁棒性方面的效力。