ARC: Anchored Representation Clouds for High-Resolution INR Classification

• URL: http://arxiv.org/abs/2503.15156v1
• Date: Wed, 19 Mar 2025 12:24:29 GMT
• Title: ARC: Anchored Representation Clouds for High-Resolution INR Classification
• Authors: Joost Luijmes, Alexander Gielisse, Roman Knyazhitskiy, Jan van Gemert,
• Abstract summary: Implicit neural representations encode signals in neural network weights as memory-efficient representation.<n>Current INR image classification methods are sensitive to image-space transformations.<n>We propose ARC: Anchored Representation Clouds, a novel INR architecture that explicitly anchors latent vectors locally in image-space.
• Score: 49.11170948406405
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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