Robustifying Fourier Features Embeddings for Implicit Neural Representations

• URL: http://arxiv.org/abs/2502.05482v1
• Date: Sat, 08 Feb 2025 07:43:37 GMT
• Title: Robustifying Fourier Features Embeddings for Implicit Neural Representations
• Authors: Mingze Ma, Qingtian Zhu, Yifan Zhan, Zhengwei Yin, Hongjun Wang, Yinqiang Zheng,
• Abstract summary: Implicit Neural Representations (INRs) employ neural networks to represent continuous functions by mapping coordinates to the corresponding values of the target function. INRs face a challenge known as spectral bias when dealing with scenes containing varying frequencies. We propose the use of multi-layer perceptrons (MLPs) without additive.
• Score: 25.725097757343367
• License:
• Abstract: Implicit Neural Representations (INRs) employ neural networks to represent continuous functions by mapping coordinates to the corresponding values of the target function, with applications e.g., inverse graphics. However, INRs face a challenge known as spectral bias when dealing with scenes containing varying frequencies. To overcome spectral bias, the most common approach is the Fourier features-based methods such as positional encoding. However, Fourier features-based methods will introduce noise to output, which degrades their performances when applied to downstream tasks. In response, this paper initially hypothesizes that combining multi-layer perceptrons (MLPs) with Fourier feature embeddings mutually enhances their strengths, yet simultaneously introduces limitations inherent in Fourier feature embeddings. By presenting a simple theorem, we validate our hypothesis, which serves as a foundation for the design of our solution. Leveraging these insights, we propose the use of multi-layer perceptrons (MLPs) without additive

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