A Theoretically Sound Upper Bound on the Triplet Loss for Improving the Efficiency of Deep Distance Metric Learning

18 April 2019

Papers citing "A Theoretically Sound Upper Bound on the Triplet Loss for Improving the Efficiency of Deep Distance Metric Learning"

8 / 8 papers shown

Title
Semantic-Enhanced Relational Metric Learning for Recommender Systems Mingming Li Fuqing Zhu Feng Yuan Songlin Hu 40 0 0 07 Jun 2024
Bayesian Learning-driven Prototypical Contrastive Loss for Class-Incremental Learning N. Raichur Lucas Heublein Tobias Feigl A. Rügamer Christopher Mutschler Felix Ott CLL BDL 73 7 0 17 May 2024
Representation Learning for Tablet and Paper Domain Adaptation in Favor of Online Handwriting Recognition Felix Ott David Rügamer Lucas Heublein Bernd Bischl Christopher Mutschler OOD 14 1 0 16 Jan 2023
Deep Metric Learning with Chance Constraints Y. Z. Gürbüz Ogul Can Aydin Alatan 19 2 0 19 Sep 2022
Zero-Shot Learning on 3D Point Cloud Objects and Beyond A. Cheraghian Shafin Rahman T. Chowdhury Dylan Campbell L. Petersson 3DPC 19 58 0 11 Apr 2021
NPT-Loss: A Metric Loss with Implicit Mining for Face Recognition S. S. Khalid Muhammad Awais Chi-Ho Chan Zhenhua Feng Ammarah Farooq A. Akbari J. Kittler CVBM 11 10 0 05 Mar 2021
Deep multi-metric learning for text-independent speaker verification Jiwei Xu Xinggang Wang Bin Feng Wenyu Liu 41 25 0 17 Jul 2020
Beyond Triplet Loss: Meta Prototypical N-tuple Loss for Person Re-identification Zhizheng Zhang Cuiling Lan Wenjun Zeng Zhibo Chen Shih-Fu Chang 30 12 0 08 Jun 2020