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首页> 外文会议>IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops >Encapsulating the Impact of Transfer Learning, Domain Knowledge and Training Strategies in Deep-Learning Based Architecture: A Biometric Based Case Study
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Encapsulating the Impact of Transfer Learning, Domain Knowledge and Training Strategies in Deep-Learning Based Architecture: A Biometric Based Case Study

机译：封装在基于深度学习的架构中的转移学习，领域知识和培训策略的影响：基于生物识别的案例研究

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摘要

In this paper, efforts have been made to analyze the impact of training strategies, transfer learning and domain knowledge on two biometric-based problems namely: three class oculus classification and fingerprint sensor classification. For analyzing these problems we have considered deep-learning based architecture and evaluated our results on benchmark contact-lens datasets like IIIT-D, ND, IIT-K (our model is publicly available) and on fingerprint datasets like FVC-2002, FVC-2004, FVC-2006, IIITD-MOLF, IITK. In-depth feature analysis of various proposed deep-learning models has been done in order to infer that indeed training in different ways along with transfer learning and domain knowledge plays a vital role in deciding the learning ability of any network.
机译：在本文中，已经努力分析培训策略，转移学习和域知识对两个基于生物识别问题的影响，即：三级Oculus分类和指纹传感器分类。为了分析这些问题，我们考虑了基于深度学习的架构，并评估了我们在IIT-D，ND，IIT-K（我们的型号公开可用）等基准接触镜头数据集上的结果，如FVC-2002，FVC- 2004年，FVC-2006，IIITD-MOLF，IITK。已经完成了各种建议的深学习模型的深入特征分析，以推断以不同方式以及传输学习和域知识在决定任何网络的学习能力方面发挥着重要作用的培训。

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