Abstract
Deep learning has shown substantial progress in image analysis. However, the computational demands of large, fully trained models remain a consideration. Transfer learning offers a strategy for adapting pre-trained models to new tasks. Traditional fine-tuning often involves updating all model parameters, which can potentially lead to overfitting and higher computational costs. This paper introduces BioTune, an evolutionary adaptive fine-tuning technique that selectively fine-tunes layers to enhance transfer learning efficiency. BioTune employs an evolutionary algorithm to identify a focused set of layers for fine-tuning, aiming to optimize model performance on a given target task. Evaluation across nine image classification datasets from various domains indicates that BioTune achieves competitive or improved accuracy and efficiency compared to existing fine-tuning methods such as AutoRGN and LoRA. By concentrating the fine-tuning process on a subset of relevant layers, BioTune reduces the number of trainable parameters, potentially leading to decreased computational cost and facilitating more efficient transfer learning across diverse data characteristics and distributions.
Abstract (translated)
深度学习在图像分析方面取得了显著进展。然而，大型全训练模型的计算需求仍然需要考虑。迁移学习为将预训练模型适应新任务提供了一种策略。传统的微调方法通常涉及更新所有模型参数，这可能会导致过拟合和更高的计算成本。本文介绍了BioTune，这是一种基于进化算法的自适应微调技术，能够选择性地对特定层进行微调以增强迁移学习效率。BioTune通过使用进化算法来识别一组聚焦的层来进行微调，旨在优化模型在给定目标任务上的性能。跨九个来自不同领域的图像分类数据集的评估结果表明，与现有的AutoRGN和LoRA等微调方法相比，BioTune在精度和效率方面达到了竞争或改进的效果。通过将微调过程集中在相关层的一个子集中，BioTune减少了可训练参数的数量，这可能会降低计算成本，并促进跨不同数据特征和分布的更高效的迁移学习。
URL
https://arxiv.org/abs/2508.15367
