Neurons Merging Layer: Towards Progressive Redundancy Reduction for Deep Supervised Hashing
文献类型:会议论文
作者 | Fu, Chaoyou1,3,4; Song, Liangchen1; Wu, Xiang4; Wang, Guoli1; He, Ran2,3,4 |
出版日期 | 2019 |
会议日期 | 2019.8.10 |
会议地点 | 中国澳门 |
英文摘要 | Deep supervised hashing has become an active topic in information retrieval. It generates hashing bits by the output neurons of a deep hashing network. During binary discretization, there often exists much redundancy between hashing bits that degenerates retrieval performance in terms of both storage and accuracy. This paper proposes a simple yet effective Neurons Merging Layer (NM-Layer) for deep supervised hashing. A graph is constructed to represent the redundancy relationship between hashing bits that is used to guide the learning of a hashing network. Specifically, it is dynamically learned by a novel mechanism defined in our active and frozen phases. According to the learned relationship, the NMLayer merges the redundant neurons together to balance the importance of each output neuron. Moreover, multiple NMLayers are progressively trained for a deep hashing network to learn a more compact hashing code from a long redundant code. Extensive experiments on four datasets demonstrate that our proposed method outperforms state-of-the-art hashing methods. |
源URL | [http://ir.ia.ac.cn/handle/173211/48688] |
专题 | 自动化研究所_智能感知与计算研究中心 |
通讯作者 | He, Ran |
作者单位 | 1.Horizon Robotics 2.Center for Excellence in Brain Science and Intelligence Technology, CAS 3.University of Chinese Academy of Sciences 4.NLPR & CRIPAC, Institute of Automation, Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Fu, Chaoyou,Song, Liangchen,Wu, Xiang,et al. Neurons Merging Layer: Towards Progressive Redundancy Reduction for Deep Supervised Hashing[C]. 见:. 中国澳门. 2019.8.10. |
入库方式: OAI收割
来源:自动化研究所
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