提升隐式神经表示在权重扰动下的鲁棒性，显著改善重建质量。
Enhancing Robustness of Implicit Neural Representations Against Weight Perturbations
- 通过最小化扰动前后损失差异，设计新鲁棒损失函数。
- 在多种模态下实验，噪声条件下PSNR提升最高达7.5 dB。
- 适合关注隐式表示稳定性与实际部署的科研人员。
隐式神经表示（INRs）利用神经网络以连续方式编码离散信号，在多媒体应用中具有重要价值。然而，其对权重扰动的敏感性成为实际部署的关键挑战。本文首次系统研究了INRs的鲁棒性，发现微小扰动即导致信号重建质量显著下降。为此，我们提出一种新方法，通过最小化扰动前后损失的差异来建模鲁棒性，并推导出一种新型鲁棒损失函数，以调控重建损失对权重的梯度，从而增强模型稳定性。在多模态重建任务上的大量实验表明，该方法在噪声条件下相比原始INRs可实现高达7.5 dB的峰值信噪比（PSNR）提升。
原文摘要 · Abstract (English)
Implicit Neural Representations (INRs) encode discrete signals in a continuous manner using neural networks, demonstrating significant value across various multimedia applications. However, the vulnerability of INRs presents a critical challenge for their real-world deployments, as the network weights might be subjected to unavoidable perturbations. In this work, we investigate the robustness of INRs for the first time and find that even minor perturbations can lead to substantial performance degradation in the quality of signal reconstruction. To mitigate this issue, we formulate the robustness problem in INRs by minimizing the difference between loss with and without weight perturbations. Furthermore, we derive a novel robust loss function to regulate the gradient of the reconstruction loss with respect to weights, thereby enhancing the robustness. Extensive experiments on reconstruction tasks across multiple modalities demonstrate that our method achieves up to a 7.5~dB improvement in peak signal-to-noise ratio (PSNR) values compared to original INRs under noisy conditions.
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