The many variations of Implicit Neural Representations (INRs), where a neural network is trained as a continuous representation of a signal, have tremendous practical utility for downstream tasks including novel view synthesis, video compression, and image superresolution. Unfortunately, the inner workings of these networks are seriously under-studied. Our work, eXplaining the Implicit Neural Canvas (XINC), is a unified framework for explaining properties of INRs by examining the strength of each neuron's contribution to each output pixel. We call the aggregate of these contribution maps the Implicit Neural Canvas and we use this concept to demonstrate that the INRs which we study learn to ''see'' the frames they represent in surprising ways. For example, INRs tend to have highly distributed representations. While lacking high-level object semantics, they have a significant bias for color and edges, and are almost entirely space-agnostic. We arrive at our conclusions by examining how objects are represented across time in video INRs, using clustering to visualize similar neurons across layers and architectures, and show that this is dominated by motion. These insights demonstrate the general usefulness of our analysis framework. Our project page is available at https://namithap10.github.io/xinc.

翻译：隐式神经表征（INR）的诸多变体（即训练神经网络作为信号的连续表征）在新视图合成、视频压缩和图像超分辨率等下游任务中具有巨大的实用价值。然而，这些网络的内在机制尚未得到充分研究。我们的工作——解释隐式神经画布（XINC）——是一个统一框架，通过考察每个神经元对每个输出像素的贡献强度来解释INR的特性。我们将这些贡献图的集合称为隐式神经画布，并利用这一概念证明，我们所研究的INR以令人惊讶的方式“观察”其表征的帧。例如，INR倾向于具有高度分布式的表征。尽管缺乏高层级物体语义，但它们对颜色和边缘具有显著偏好，且几乎完全与空间无关。我们通过考察物体在视频INR中随时间变化的表征方式得出这些结论，利用聚类方法可视化跨层和跨架构的相似神经元，并表明运动在其中起主导作用。这些见解证明了我们分析框架的通用性。我们的项目页面见https://namithap10.github.io/xinc。