SSR: An Efficient and Robust Framework for Learning with Unknown Label Noise C Feng, G Tzimiropoulos, I Patras BMVC, 2022 | 35* | 2022 |
Adaptive soft contrastive learning C Feng, I Patras ICPR, 2022 | 25 | 2022 |
MaskCon: Masked Contrastive Learning for Coarse-Labelled Dataset C Feng, I Patras CVPR, 2023 | 12 | 2023 |
Self-Supervised Representation Learning with Cross-Context Learning between Global and Hypercolumn Features Z Gao, C Feng, I Patras WACV, 2024 | 5 | 2024 |
LAFS: Landmark-based Facial Self-supervised Learning for Face Recognition Z Sun, C Feng, I Patras, G Tzimiropoulos CVPR, 2024 | 4 | 2024 |
NoiseBox: Towards More Efficient and Effective Learning with Noisy Labels C Feng, G Tzimiropoulos, I Patras IEEE Transactions on Circuits and Systems for Video Technology, 2024 | 2 | 2024 |
CLIPCleaner: Cleaning Noisy Labels with CLIP C Feng, G Tzimiropoulos, I Patras ACM MM, 2024 | 1 | 2024 |
PROSAC: Provably Safe Certification for Machine Learning Models under Adversarial Attacks C Feng, Z Liu, Z Zhi, I Bogunovic, C Gerner-Beuerle, M Rodrigues AAAI, 2025 | | 2025 |
Towards robust and efficient representation learning with imperfect labels C Feng Queen Mary, University of London, 2024 | | 2024 |
Towards Better Understanding Open-set Noise in Learning with Noisy Labels C Feng, N Sebe, I Patras | | 2024 |