EVOS: Efficient Implicit Neural Training via EVOlutionary Selector

TL;DR

EVOS is a novel evolutionary-based training method that accelerates implicit neural representations by selectively training on the most informative samples, significantly reducing computation while maintaining or improving convergence.

Contribution

This paper introduces EVOS, a new evolutionary sampling approach that redefines sample selection for efficient INR training, achieving substantial speedups without sacrificing accuracy.

Findings

Reduces training time by 48%-66%.

Achieves state-of-the-art acceleration among sampling strategies.

Maintains superior convergence performance.

Abstract

We propose EVOlutionary Selector (EVOS), an efficient training paradigm for accelerating Implicit Neural Representation (INR). Unlike conventional INR training that feeds all samples through the neural network in each iteration, our approach restricts training to strategically selected points, reducing computational overhead by eliminating redundant forward passes. Specifically, we treat each sample as an individual in an evolutionary process, where only those fittest ones survive and merit inclusion in training, adaptively evolving with the neural network dynamics. While this is conceptually similar to Evolutionary Algorithms, their distinct objectives (selection for acceleration vs. iterative solution optimization) require a fundamental redefinition of evolutionary mechanisms for our context. In response, we design sparse fitness evaluation, frequency-guided crossover, and augmented unbiased mutation to comprise EVOS. These components respectively guide sample selection with reduced computational cost, enhance performance through frequency-domain balance, and mitigate selection bias from cached evaluation. Extensive experiments demonstrate that our method achieves approximately 48%-66% reduction in training time while ensuring superior convergence without additional cost, establishing state-of-the-art acceleration among recent sampling-based strategies.