4.7 Article

Regularized Hardmining loss for face recognition

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Engineering, Electrical & Electronic

Orthogonality Loss: Learning Discriminative Representations for Face Recognition

Shanming Yang et al.

Summary: This article introduces a new loss function - Orthogonality loss, which increases inter-class variance by penalizing weight vectors to learn discriminative representations. Empirical studies and extensive experiments show that Orthogonality loss outperforms strong baselines in face recognition, demonstrating its extensive suitability and effectiveness.

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY (2021)

Article Computer Science, Artificial Intelligence

Focal Loss for Dense Object Detection

Tsung-Yi Lin et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2020)

Article Computer Science, Artificial Intelligence

Minimum margin loss for deep face recognition

Xin Wei et al.

PATTERN RECOGNITION (2020)

Article Engineering, Electrical & Electronic

Additive Margin Softmax for Face Verification

Feng Wang et al.

IEEE SIGNAL PROCESSING LETTERS (2018)

Proceedings Paper Energy & Fuels

Space Charge Analysis of Polyethylene with Chemical Defects Based on Density Function Theory

Tao Lin et al.

2018 IEEE INTERNATIONAL CONFERENCE ON HIGH VOLTAGE ENGINEERING AND APPLICATION (ICHVE) (2018)

Article Engineering, Electrical & Electronic

Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks

Kaipeng Zhang et al.

IEEE SIGNAL PROCESSING LETTERS (2016)