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One-stage object detection knowledge distillation via adversarial learning

文献类型：期刊论文

作者Dong, Na1; Zhang, Yongqiang1; Ding, Mingli1; Xu, Shibiao2; Bai, Yancheng3
刊名APPLIED INTELLIGENCE
出版日期2021-07-24
页码17
关键词Knowledge distillation Object detection Generative adversarial learning
ISSN号0924-669X
DOI10.1007/s10489-021-02634-6
通讯作者Zhang, Yongqiang(yongqiang.zhang.hit@gmail.com)
英文摘要Impressive methods for object detection tasks have been proposed based on convolutional neural networks (CNNs), however, they usually use very computation expensive deep networks to obtain such significant performance. Knowledge distillation has attracted much attention in the task of image classification lately since it can use compact models that reduce computations while preserving performance. Moreover, the best performing deep neural networks often assemble the outputs of multiple networks in an average way. However, the memory required to store these networks, and the time required to execute them in inference, which prohibits these methods used in real-time applications. In this paper, we present a knowledge distillation method for one-stage object detection, which can assemble a variety of large, complex trained networks into a lightweight network. In order to transfer diverse knowledge from various trained one-stage object detection networks, an adversarial-based learning strategy is employed as supervision to guide and optimize the lightweight student network to recover the knowledge of teacher networks, and to enable the discriminator module to distinguish the feature of teacher and student simultaneously. The proposed method exhibits two predominant advantages: (1) The lightweight student model can learn the knowledge of the teacher, which contains richer discriminative information than the model trained from scratch. (2) Faster inference speed than traditional ensemble methods from multiple networks is realized. A large number of experiments are carried out on PASCAL VOC and MS COCO datasets to verify the effectiveness of the proposed method for one-stage object detection, which obtains 3.43%, 2.48%, and 5.78% mAP promotions for vgg11-ssd, mobilenetv1-ssd-lite and mobilenetv2-ssd-lite student network on the PASCAL VOC 2007 dataset, respectively. Furthermore, with multi-teacher ensemble method, vgg11-ssd gains 7.10% improvement, which is remarkable.
WOS关键词DEEP ; NETWORK ; FUSION
资助项目China Postdoctoral Science Foundation[259822]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000677240200002
出版者SPRINGER
资助机构China Postdoctoral Science Foundation
源URL[http://ir.ia.ac.cn/handle/173211/45569]  
专题模式识别国家重点实验室_三维可视计算
通讯作者Zhang, Yongqiang
作者单位1.Harbin Inst Technol, Sch Instrument Sci & Engn, Harbin, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Software, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Dong, Na,Zhang, Yongqiang,Ding, Mingli,et al. One-stage object detection knowledge distillation via adversarial learning[J]. APPLIED INTELLIGENCE,2021:17.
APA Dong, Na,Zhang, Yongqiang,Ding, Mingli,Xu, Shibiao,&Bai, Yancheng.(2021).One-stage object detection knowledge distillation via adversarial learning.APPLIED INTELLIGENCE,17.
MLA Dong, Na,et al."One-stage object detection knowledge distillation via adversarial learning".APPLIED INTELLIGENCE (2021):17.

入库方式： OAI收割

来源：自动化研究所

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