【摘要】 基于监督学习的深度神经网络焊接技术在实际工业场景中，难以获取带有标签的数据样本，限制了深度网络模型的性能。而针对无标注或不完整标注数据集，迁移学习算法提供了一种新颖的解决方案。为此，从领域自适应和预训练-微调两个方面阐述了迁移学习算法，并从近年来算法发展历程和在焊接熔透识别和缺陷检测中的应用方面归纳总结了当前已有的研究工作。同时，还探讨了焊接领域未来需要更多关注和探索的迁移学习问题。通过迁移学习的方法，可以更好地利用已有数据和知识，提升深度学习模型在焊接领域的效果及焊接熔透识别和焊接缺陷检测的准确度，从而推动智能焊接制造技术的发展。更多还原

【Abstract】 Deep neural network techniques based on supervised learning have been extensively employed in the field of welding. However, in real industrial scenarios, obtaining labeled data samples is challenging, which limits the performance of deep network models. For unlabeled or partially labeled datasets, transfer learning algorithms offer a novel solution. Transfer learning algorithms in terms of domain adaptation and pre-training-fine-tuning are introduced and a summary of current research on their development over recent years and their applications in weld penetration identification and defect detection is provided. And transfer learning issues in welding that require further attention and exploration in the futureis highlighted. Transfer learning methods can enhance the effectiveness of deep learning models by better utilizing existing data and knowledge in the welding field, specifically in weld penetration recognition and welding defect detection accuracy. This promotes the development of intelligent welding manufacturing technology.更多还原