Correction: Variational Rectification Inference for Learning with Noisy Labels.
Int. J. Comput. Vis., March, 2025
Variational Rectification Inference for Learning with Noisy Labels.
Int. J. Comput. Vis., February, 2025
Influence-Based Fair Selection for Sample-Discriminative Backdoor Attack.
Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025
Converting Artificial Neural Networks to Ultralow-Latency Spiking Neural Networks for Action Recognition.
IEEE Trans. Cogn. Dev. Syst., August, 2024
Correction: Learning sample-aware threshold for semi-supervised learning.
Mach. Learn., July, 2024
Learning sample-aware threshold for semi-supervised learning.
Mach. Learn., July, 2024
BDetCLIP: Multimodal Prompting Contrastive Test-Time Backdoor Detection.
CoRR, 2024
Debiased Sample Selection for Combating Noisy Labels.
CoRR, 2024
Candidate Pseudolabel Learning: Enhancing Vision-Language Models by Prompt Tuning with Unlabeled Data.
Proceedings of the Forty-first International Conference on Machine Learning, 2024
Improving Generalization in Meta-Learning via Meta-Gradient Augmentation.
CoRR, 2023
Fine-Grained Classification with Noisy Labels.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023
Learning to rectify for robust learning with noisy labels.
Pattern Recognit., 2022
Self-Filtering: A Noise-Aware Sample Selection for Label Noise with Confidence Penalization.
Proceedings of the Computer Vision - ECCV 2022, 2022