A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection 笔记
2017-07-25 09:13
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这篇paper主要是针对训练数据时，遮蔽和形变物体的数据集不一定很全面提出的，于是，想到可以用生成网络生成这种数据来进行训练。但是这种数据生成又是很困难的，作者就想到跳过生成数据，直接在Feature Map上做类似的生成。
和生成模型很类似，在Feature Map上引入Adversary去和Detector做对抗训练，训练出最终的模型。
详细的内容可参见paper：
A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
找到一篇caffe的github实现：
https://github.com/xiaolonw/adversarial-frcnn
和生成模型很类似，在Feature Map上引入Adversary去和Detector做对抗训练，训练出最终的模型。
详细的内容可参见paper：
A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
找到一篇caffe的github实现：
https://github.com/xiaolonw/adversarial-frcnn
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