Generalizable Implicit Neural Representations via Instance Pattern Composers

• URL: http://arxiv.org/abs/2211.13223v2
• Date: Mon, 17 Apr 2023 12:55:57 GMT
• Title: Generalizable Implicit Neural Representations via Instance Pattern Composers
• Authors: Chiheon Kim, Doyup Lee, Saehoon Kim, Minsu Cho, Wook-Shin Han
• Abstract summary: We introduce a simple yet effective framework for generalizable implicit neural representations (INRs) Our framework is fully compatible with existing meta-learning and hypernetworks in learning to predict the weight for unseen instances. Our method achieves high performance on a wide range of domains such as an audio, image, and 3D object, while the ablation study validates our weight modulation.
• Score: 40.04085054791994
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite recent advances in implicit neural representations (INRs), it remains challenging for a coordinate-based multi-layer perceptron (MLP) of INRs to learn a common representation across data instances and generalize it for unseen instances. In this work, we introduce a simple yet effective framework for generalizable INRs that enables a coordinate-based MLP to represent complex data instances by modulating only a small set of weights in an early MLP layer as an instance pattern composer; the remaining MLP weights learn pattern composition rules for common representations across instances. Our generalizable INR framework is fully compatible with existing meta-learning and hypernetworks in learning to predict the modulated weight for unseen instances. Extensive experiments demonstrate that our method achieves high performance on a wide range of domains such as an audio, image, and 3D object, while the ablation study validates our weight modulation.

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