提出低秩专家混合模型，提升多扰动对抗训练的鲁棒性与泛化能力。
RoME: Robust Mixture of Low-Rank Experts against Multiple Adversarial Perturbations
- 用低秩更新设计专家，分离共性与特异性特征
- 双尺度门控+威胁引导多样化，解决路由同质化问题
- 在多种扰动下表现更优，尤其对未知威胁有强泛化力
多扰动对抗训练（MAT）旨在提升对多种ℓ_p扰动的鲁棒性，但不同威胁间存在鲁棒性权衡。本文采用专家混合（MoE）机制，将不同威胁分配至独立路径。然而，传统MoE面临两大挑战：专家易忽略威胁特异性特征，且冗余捕捉共性特征；门控网络产生威胁无关的路由行为，导致路径缺乏针对性。为此，我们提出鲁棒低秩专家混合（RoME），每个专家以低秩加性更新共享主干，既能捕获共性特征，又聚焦威胁特异性信息。为解决门控同质化问题，RoME引入：(i) 双尺度门控，利用局部与全局特征中的威胁区分信号；(ii) 威胁引导的门控多样化，强制不同威胁下专家使用差异显著。大量实验表明，RoME在联合鲁棒性与自然准确率上超越现有最先进MAT方法，并显著提升对未见威胁的鲁棒性。代码已开源。
原文摘要 · Abstract (English)
Multi-perturbation adversarial training (MAT) aims to achieve robustness against multiple $\ell_p$ perturbations but suffers from robustness trade-offs between different threats. To address this, we employ a mixture of experts (MoE) to route different threats through distinct model pathways. However, naive application of MoE encounters two critical challenges: experts tend to overlook threat-specific features and redundantly capture features shared across threats, and gating networks suffer from threat-agnostic routing where they learn nearly identical routing patterns across threats, thus preventing the construction of threat-specific model pathways. To this end, we propose Robust Mixture of Low-Rank Experts (RoME), where each expert is a low-rank additive update to the shared backbone, allowing it to capture threat-common features while experts focus on threat-specific information. To address threat-agnostic routing, RoME introduces (i) dual-scale gating that exploits threat-discriminative signals from local and global level features, and (ii) threat-guided gating diversification that enforces diverse expert utilization across threats. Extensive experiments demonstrate that RoME outperforms existing state-of-the-art MAT in union robustness and natural accuracy and improves robustness against unseen threats. Codes are available at https://github.com/wkim97/RoME.
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