Implicit Neural Representation (INR), which utilizes a neural network to map
coordinate inputs to corresponding attributes, is causing a revolution in the
field of signal processing. However, current INR techniques suffer from a
restricted capability to tune their supported frequency set, resulting in
imperfect performance when representing complex signals with multiple
frequencies. We have identified that this frequency-related problem can be
greatly alleviated by introducing variable-periodic activation functions, for
which we propose FINER. By initializing the bias of the neural network within
different ranges, sub-functions with various frequencies in the
variable-periodic function are selected for activation. Consequently, the
supported frequency set of FINER can be flexibly tuned, leading to improved
performance in signal representation. We demonstrate the capabilities of FINER
in the contexts of 2D image fitting, 3D signed distance field representation,
and 5D neural radiance fields optimization, and we show that it outperforms
existing INRs.

利用神经网络将坐标输入映射到相应属性的隐性神经表示（INR）正在信号处理领域引起革命。然而，当前的 INR 技术在调整其支持的频率集方面存在局限性，导致在表示具有多个频率的复杂信号时性能不完善。我们发现，通过引入变周期激活函数，可以大大缓解这个与频率相关的问题，并提出 FINER。通过在不同范围内初始化神经网络的偏置，选择变周期函数中具有不同频率的子函数进行激活。因此，FINER 的支持频率集可以灵活调节，从而提高信号表示性能。我们在二维图像拟合、三维有向距离场表示和五维神经辐射场优化的背景下展示了 FINER 的能力，并证明它优于现有的 INR。