Implicit neural representations (INRs) encode signals in neural network weights as a memory-efficient representation, decoupling sampling resolution from the associated resource costs. Current INR image classification methods are demonstrated on low-resolution data and are sensitive to image-space transformations. We attribute these issues to the global, fully-connected MLP neural network architecture encoding of current INRs, which lack mechanisms for local representation: MLPs are sensitive to absolute image location and struggle with high-frequency details. We propose ARC: Anchored Representation Clouds, a novel INR architecture that explicitly anchors latent vectors locally in image-space. By introducing spatial structure to the latent vectors, ARC captures local image data which in our testing leads to state-of-the-art implicit image classification of both low- and high-resolution images and increased robustness against image-space translation. Code can be found at https://github.com/JLuij/anchored_representation_clouds.
@article{arxiv.2503.15156,
title = {ARC: Anchored Representation Clouds for High-Resolution INR Classification},
author = {Joost Luijmes and Alexander Gielisse and Roman Knyazhitskiy and Jan van Gemert},
journal= {arXiv preprint arXiv:2503.15156},
year = {2025}
}
Comments
Accepted at the ICLR 2025 Workshop on Neural Network Weights as a New Data Modality