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。Dual-Aligned Feature Confusion Alleviation for Generalized Zero-Shot Learning
Hongzu Su et al.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY (2023)
Kernel-based online regression with canal loss
Xijun Liang et al.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2022)
Investigating the Bilateral Connections in Generative Zero-Shot Learning
Jingjing Li et al.
IEEE TRANSACTIONS ON CYBERNETICS (2022)
Valley-loss regular simplex support vector machine for robust multiclass classification
Long Tang et al.
KNOWLEDGE-BASED SYSTEMS (2021)
Dual VAEGAN: A generative model for generalized zero-shot learning
Yuxuan Luo et al.
APPLIED SOFT COMPUTING (2021)
Contrastive Embedding for Generalized Zero-Shot Learning
Zongyan Han et al.
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021 (2021)
Semi-Supervised Low-Rank Semantics Grouping for Zero-Shot Learning
Bingrong Xu et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING (2021)
Robust support vector regression with generic quadratic nonconvex ε-insensitive loss
Yafen Ye et al.
APPLIED MATHEMATICAL MODELLING (2020)
Zero-Shot Learning-A Comprehensive Evaluation of the Good, the Bad and the Ugly
Yongqin Xian et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2019)
Zero-Shot Learning via Robust Latent Representation and Manifold Regularization
Min Meng et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING (2019)
Scalable Zero-Shot Learning via Binary Visual-Semantic Embeddings
Fumin Shen et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING (2019)
Rethinking Knowledge Graph Propagation for Zero-Shot Learning
Michael Kampffmeyer et al.
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019) (2019)
Ramp-loss nonparallel support vector regression: Robust, sparse and scalable approximation
Long Tang et al.
KNOWLEDGE-BASED SYSTEMS (2018)
Iterative Cross Learning on Noisy Labels
Bodi Yuan et al.
2018 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2018) (2018)
A Semi-Supervised Two-Stage Approach to Learning from Noisy Labels
Yifan Ding et al.
2018 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2018) (2018)
A robust algorithm of support vector regression with a trimmed Huber loss function in the primal
Chuanfa Chen et al.
SOFT COMPUTING (2017)
Learning Discriminative Latent Attributes for Zero-Shot Classification
Huajie Jiang et al.
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV) (2017)
Semantic Autoencoder for Zero-Shot Learning
Elyor Kodirov et al.
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)
Classification with Noisy Labels by Importance Reweighting
Tongliang Liu et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2016)
Ramp loss least squares support vector machine
Dalian Liu et al.
JOURNAL OF COMPUTATIONAL SCIENCE (2016)
Ramp loss nonparallel support vector machine for pattern classification
Dalian Liu et al.
KNOWLEDGE-BASED SYSTEMS (2015)
Learning from multiple annotators with varying expertise
Yan Yan et al.
MACHINE LEARNING (2014)
Training robust support vector regression with smooth non-convex loss function
Ping Zhong
OPTIMIZATION METHODS & SOFTWARE (2012)
Noise-tolerant learning, the parity problem, and the statistical query model
A Blum et al.
JOURNAL OF THE ACM (2003)
The concave-convex procedure
AL Yuille et al.
NEURAL COMPUTATION (2003)