通过对称幂变换提升隐式神经表示的表达能力。
Enhancing Implicit Neural Representations via Symmetric Power Transformation
- 引入非线性对称幂变换，实现数据范围与对称性的协同优化。
- 在1D音频、2D图像、3D视频任务中均显著提升INR拟合性能。
- 方法可逆且无需额外存储，适合高维数据建模场景。
我们提出对称幂变换，从数据变换角度增强隐式神经表示（INR）的表达能力。不同于以往使用随机排列或索引重排的方法，本方法为可逆操作，不增加额外存储开销。我们首先研究了有利于INR训练的数据特性，提出范围定义对称假设，认为特定范围与对称性可提升INR的表达能力。基于此，设计非线性对称幂变换，利用幂系数将数据重分布以逼近目标范围内的对称性。为进一步提升鲁棒性，引入偏差感知校准与自适应软边界机制，缓解极端偏差放大和连续性破坏问题。大量实验验证了该方法的有效性，在1D音频、2D图像和3D视频拟合任务中均优于其他数据变换策略，能可靠提升INR性能。
原文摘要 · Abstract (English)
We propose symmetric power transformation to enhance the capacity of Implicit Neural Representation~(INR) from the perspective of data transformation. Unlike prior work utilizing random permutation or index rearrangement, our method features a reversible operation that does not require additional storage consumption. Specifically, we first investigate the characteristics of data that can benefit the training of INR, proposing the Range-Defined Symmetric Hypothesis, which posits that specific range and symmetry can improve the expressive ability of INR. Based on this hypothesis, we propose a nonlinear symmetric power transformation to achieve both range-defined and symmetric properties simultaneously. We use the power coefficient to redistribute data to approximate symmetry within the target range. To improve the robustness of the transformation, we further design deviation-aware calibration and adaptive soft boundary to address issues of extreme deviation boosting and continuity breaking. Extensive experiments are conducted to verify the performance of the proposed method, demonstrating that our transformation can reliably improve INR compared with other data transformations. We also conduct 1D audio, 2D image and 3D video fitting tasks to demonstrate the effectiveness and applicability of our method.
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