Abstract
Deep learning has significantly advanced image analysis across diverse domains but often depends on large, annotated datasets for success. Transfer learning addresses this challenge by utilizing pre-trained models to tackle new tasks with limited labeled data. However, discrepancies between source and target domains can hinder effective transfer learning. We introduce BioTune, a novel adaptive fine-tuning technique utilizing evolutionary optimization. BioTune enhances transfer learning by optimally choosing which layers to freeze and adjusting learning rates for unfrozen layers. Through extensive evaluation on nine image classification datasets, spanning natural and specialized domains such as medical imaging, BioTune demonstrates superior accuracy and efficiency over state-of-the-art fine-tuning methods, including AutoRGN and LoRA, highlighting its adaptability to various data characteristics and distribution changes. Additionally, BioTune consistently achieves top performance across four different CNN architectures, underscoring its flexibility. Ablation studies provide valuable insights into the impact of BioTune's key components on overall performance. The source code is available at this https URL.
Abstract (translated)
深度学习在各种领域中显著推进了图像分析的发展，但通常依赖于大规模且标注完整的数据集才能取得成功。迁移学习通过利用预训练的模型来解决这个问题，在新任务上即使有少量标记数据也能发挥作用。然而，源域和目标域之间的差异会阻碍有效迁移学习的应用。 我们引入了一种新的自适应微调技术BioTune，它使用了进化优化方法。BioTune通过最优地选择要冻结哪些层以及调整未冻结层的学习率来增强迁移学习的效果。通过对九个图像分类数据集的广泛评估（包括自然和专门化领域如医学成像），BioTune展示了比当前最先进的微调方法（例如AutoRGN和LoRA）更高的准确性和效率，突显了它适应不同数据特性和分布变化的能力。此外，无论使用四种不同的CNN架构中的哪一种，BioTune都能保持顶级性能，这证明了它的灵活性。 消融研究为理解BioTune关键组件对整体性能的影响提供了宝贵的见解。源代码可在以下链接获得：[此链接]（请将方括号内的文本替换为您实际提供的GitHub或其他版本控制平台的URL）。
URL
https://arxiv.org/abs/2601.11235
