ML Anthology

Encapsulating the Impact of Transfer Learning, Domain Knowledge and Training Strategies in Deep-Learning Based Architecture: A Biometric Based Case Study

Avantika Singh, Aditya Nigam
CVPRW 2018 pp. 1866-1868
doi:10.1109/CVPRW.2018.00242 /cvprw/2018/singh2018cvprw-encapsulating/

Abstract

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.

CVPRW Semantic Scholar

Cite

Text

Singh and Nigam. "Encapsulating the Impact of Transfer Learning, Domain Knowledge and Training Strategies in Deep-Learning Based Architecture: A Biometric Based Case Study." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018. doi:10.1109/CVPRW.2018.00242

Markdown

[Singh and Nigam. "Encapsulating the Impact of Transfer Learning, Domain Knowledge and Training Strategies in Deep-Learning Based Architecture: A Biometric Based Case Study." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018.](https://mlanthology.org/cvprw/2018/singh2018cvprw-encapsulating/) doi:10.1109/CVPRW.2018.00242

BibTeX

@inproceedings{singh2018cvprw-encapsulating,
  title     = {{Encapsulating the Impact of Transfer Learning, Domain Knowledge and Training Strategies in Deep-Learning Based Architecture: A Biometric Based Case Study}},
  author    = {Singh, Avantika and Nigam, Aditya},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2018},
  pages     = {1866-1868},
  doi       = {10.1109/CVPRW.2018.00242},
  url       = {https://mlanthology.org/cvprw/2018/singh2018cvprw-encapsulating/}
}