Abstract
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.
Abstract (translated)
单模型系统通常在诸如演讲验证（SV）和图像分类等任务中存在不足，因此在决策过程中严重依赖先验知识，导致性能较低。尽管多模型融合（MMF）可以在一定程度上减轻这些问题，但学习到的表示的冗余可能限制了提高。为此，我们提出了一个对抗性互补表示学习（ACoRL）框架，使新训练的模型能够避免之前获得的知识，使得每个组件模型能够学习到最独特的互补表示。我们详细解释了这种方法的工作原理，并进行了实验验证，表明与传统MMF相比，我们的方法能更有效地提高性能。此外，归因分析证实，在ACoRL框架下训练的模型获得了更多的互补知识，这表明我们的方法在提高任务效率和鲁棒性方面具有有效性。
URL
https://arxiv.org/abs/2404.15704
