Fine-tuning Convolitional Neural Networks for Biomedical Image Analysis: Actively and Incrementally
本篇论文发表于CVPR2017，作者为美国亚利桑那州立大学着的在读博士生周纵苇。它主要解决的仍然是生物医学图像在用于深度学习时数据量过少的问题：如何使用尽可能少的标签数据来训练一个效果promising的分类器。作者提出了一个AIFT (active,incremental fine-tuning)网络，能够节约标注的时间和成本，把主动学习和迁移学习集成到一个框架。AIFT算法开始是直接使用一个预
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