Improved Implicit Neural Representation with Fourier Reparameterized Training
Authors: Kexuan Shi, Xingyu Zhou, Shuhang Gu

Implicit Neural Representation (INR) as a mighty representation paradigm has achieved success in various computer vision tasks recently. Due to the low-frequency bias issue of vanilla multi-layer perceptron (MLP) existing methods have investigated advanced techniques such as positional encoding and periodic activation function to improve the accuracy of INR. In this paper we connect the network training bias with the reparameterization technique and theoretically prove that weight reparameterization could provide us a chance to alleviate the spectral bias of MLP. Based on our theoretical analysis we propose a Fourier reparameterization method which learns coefficient matrix of fixed Fourier bases to compose the weights of MLP. We evaluate the proposed Fourier reparameterization method on different INR tasks with various MLP architectures including vanilla MLP MLP with positional encoding and MLP with advanced activation function etc. The superiority approximation results on different MLP architectures clearly validate the advantage of our proposed method. Armed with our Fourier reparameterization method better INR with more textures and less artifacts can be learned from the training data. The codes are available at https://github.com/LabShuHangGU/FR-INR.