论文研读--Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification
2016-10-14 17:15
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三个贡献:
我们呈现一个从多个领域中学习共同特征并在所有领域都表现优秀的流程。这使我们在同一问题上能从不同数据库中学到更好的特性。
我们使用DomainGuided Dropout来消去每个领域无用的神经元,从而提高了CNN的性能。
我们的方法在re-identification数据库识别上领先现有其它方法很多。
相关领域:
CNN特征表现
Dropout与CNN性能提升
Re-ID
算法流程:
联合学习(jointlearning)
CNN结构
DGD (Domain Guided Dropout)
移去影响分数小的神经元
论文:https://arxiv.org/abs/1604.07528
代码:https://github.com/Cysu/dgd_person_reid
我们呈现一个从多个领域中学习共同特征并在所有领域都表现优秀的流程。这使我们在同一问题上能从不同数据库中学到更好的特性。
我们使用DomainGuided Dropout来消去每个领域无用的神经元,从而提高了CNN的性能。
我们的方法在re-identification数据库识别上领先现有其它方法很多。
相关领域:
CNN特征表现
Dropout与CNN性能提升
Re-ID
算法流程:
联合学习(jointlearning)
CNN结构
DGD (Domain Guided Dropout)
移去影响分数小的神经元
论文:https://arxiv.org/abs/1604.07528
代码:https://github.com/Cysu/dgd_person_reid
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