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.

翻译：尽管隐式神经表示（INR）近期取得了进展，但对于基于坐标的多层感知机（MLP）而言，学习跨数据实例的通用表示并推广至未见实例仍具挑战性。本研究提出一种简单而有效的可泛化INR框架，通过仅调节早期MLP层中的一小部分权重作为实例模式合成器，使基于坐标的MLP能够表示复杂的数据实例；其余MLP权重则学习用于跨实例通用表示的模式组合规则。我们的可泛化INR框架与现有元学习和超网络方法完全兼容，可用于预测未见实例的调节权重。大量实验表明，该方法在音频、图像和三维物体等多个领域均取得优异性能，消融研究也验证了权重调节机制的有效性。