共 50 条
- [21] Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels [J]. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018), 2018, 31
- [25] NOISY TRAINING FOR DEEP NEURAL NETWORKS [J]. 2014 IEEE CHINA SUMMIT & INTERNATIONAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (CHINASIP), 2014, : 16 - 20
- [26] Training Neural Networks on Noisy Data [J]. ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING ICAISC 2014, PT I, 2014, 8467 : 131 - 142
- [27] DCBT-Net: Training Deep Convolutional Neural Networks With Extremely Noisy Labels [J]. IEEE ACCESS, 2020, 8 : 220482 - 220495
- [28] A Study on the Impact of Data Augmentation for Training Convolutional Neural Networks in the Presence of Noisy Labels [J]. 2022 35TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI 2022), 2022, : 25 - 30
- [29] Neighborhood-Regularized Self-Training for Learning with Few Labels [J]. THIRTY-SEVENTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 37 NO 9, 2023, : 10611 - 10619
- [30] Robust Loss Functions for Training Decision Trees with Noisy Labels [J]. THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 14, 2024, : 15859 - 15867