New Framework Improves Efficiency of Neural Field Representations Across Multiple Data Types
Researchers introduced LH-NeF, a new framework that learns tokenized representations of continuous signals using locality and hierarchy priors, addressing scalability limitations in neural field methods. Traditional approaches rely on memory-intensive meta-learning, while feed-forward alternatives sacrifice generality across different data modalities. The method achieves 42× lower memory usage and 133× larger batch sizes while matching or exceeding performance on images, 3D shapes, and climate data.
Neural fields represent data as functions mapping coordinates to values, offering a unified framework for representation learning across different data types. However, existing approaches face a fundamental trade-off: per-sample meta-learning scales poorly due to memory constraints, while feed-forward encoding typically requires modality-specific assumptions that limit generality. The proposed LH-NeF framework addresses this by injecting locality and hierarchy as priors into a modality-agnostic architecture. A locality-preserving hierarchical encoder converts raw coordinate-value observations into structured tokens for field reconstruction. By replacing meta-learning's computationally expensive inner loop with a single forward pass, LH-NeF achieves substantial efficiency gains—42× reduction in memory usage and support for 133× larger batch sizes compared to the strongest baseline. Evaluation across diverse domains (images, 3D shapes, climate fields) demonstrates that the learned representations match or exceed performance of modality-agnostic, modality-specific, and specialized generative baselines on both reconstruction and downstream tasks.
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- arXiv cs.LGCenter
Multi-resolution Enhancement for Full Spectrum Neural Representations
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