迁移学习让小数据也能实现高精度视觉任务
Transfer Learning Applied to Computer Vision Problems: Survey on Current Progress, Limitations, and Opportunities
- 复用预训练模型，减少对大量标注数据的依赖
- 在保持高准确率的同时显著降低计算资源消耗
- 适合缺乏数据和算力的CV实际应用场景
计算机视觉（CV）领域曾面临挑战：早期依赖手工特征和规则算法，导致精度有限。机器学习（ML）的引入带来了进步，特别是迁移学习（TL），通过复用预训练模型解决多种CV问题。TL在减少数据与计算需求的同时，仍能实现接近原模型的准确率，已成为CV领域的关键技术。本研究聚焦于迁移学习的发展进程，探讨其在实际CV应用中的表现、当前局限及未来机遇。
原文摘要 · Abstract (English)
The field of Computer Vision (CV) has faced challenges. Initially, it relied on handcrafted features and rule-based algorithms, resulting in limited accuracy. The introduction of machine learning (ML) has brought progress, particularly Transfer Learning (TL), which addresses various CV problems by reusing pre-trained models. TL requires less data and computing while delivering nearly equal accuracy, making it a prominent technique in the CV landscape. Our research focuses on TL development and how CV applications use it to solve real-world problems. We discuss recent developments, limitations, and opportunities.
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