迈向使用类特定专家混合提升对常见污染的鲁棒性
Towards Improving Robustness Against Common Corruptions using Mixture of Class Specific Experts
AI总结：
本文提出类特定专家混合架构，通过为每个类别训练专门网络片段并聚合输出，以增强神经网络对常见污染的鲁棒性，并强调使用常见污染基准进行全面评估的重要性。
摘要：
神经网络在各个领域已展现出显著的准确性，然而其对细微输入扰动的脆弱性仍然是一个持续存在的挑战。诸如数据增强之类的传统方法虽然在一定程度上有效，但在应对未预见的污染方面存在不足，限制了神经网络在现实场景中的适应性。为此，本文引入了一种新颖的范式，称为类特定专家混合架构。该方法涉及对各个类别进行特征学习的解耦，在可扩展性和整体性能上提供了细微的增强。通过为每个类别训练专门的网络片段并随后聚合其输出，所提出的架构旨在缓解与常见神经网络结构相关的脆弱性。研究强调了全面评估方法的重要性，倡导纳入诸如常见污染基准之类的基准。这种纳入为神经网络的脆弱性提供了细微的见解，特别是关于其泛化能力和对未预见失真的鲁棒性。该研究与更广泛的目标相一致，即推进高度鲁棒的学习系统的发展，这些系统能够在多样且具有挑战性的现实场景中进行细微的推理。通过这一贡献，本文旨在促进对神经网络局限性的更深入理解，并提出一种实用方法来增强其在不断演变和不可预测条件下的韧性。
英文摘要：
Neural networks have demonstrated significant accuracy across various domains, yet their vulnerability to subtle input alterations remains a persistent challenge. Conventional methods like data augmentation, while effective to some extent, fall short in addressing unforeseen corruptions, limiting the adaptability of neural networks in real-world scenarios. In response, this paper introduces a novel paradigm known as the Mixture of Class-Specific Expert Architecture. The approach involves disentangling feature learning for individual classes, offering a nuanced enhancement in scalability and overall performance. By training dedicated network segments for each class and subsequently aggregating their outputs, the proposed architecture aims to mitigate vulnerabilities associated with common neural network structures. The study underscores the importance of comprehensive evaluation methodologies, advocating for the incorporation of benchmarks like the common corruptions benchmark. This inclusion provides nuanced insights into the vulnerabilities of neural networks, especially concerning their generalization capabilities and robustness to unforeseen distortions. The research aligns with the broader objective of advancing the development of highly robust learning systems capable of nuanced reasoning across diverse and challenging real-world scenarios. Through this contribution, the paper aims to foster a deeper understanding of neural network limitations and proposes a practical approach to enhance their resilience in the face of evolving and unpredictable conditions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
