15 citations · 23 across the 9 of their papers we have counts for
3 papers · 1 filter
Context Label Learning: Improving Background Class Representations in Semantic Segmentation
Zeju Li, Konstantinos Kamnitsas, Cheng Ouyang +2
Background samples provide key contextual information for segmenting regions of interest (ROIs). However, they always cover a diverse set of structures, causing difficulties for th…
Estimating Model Performance under Domain Shifts with Class-Specific Confidence Scores
Zeju Li, Konstantinos Kamnitsas, Mobarakol Islam +2
Machine learning models are typically deployed in a test setting that differs from the training setting, potentially leading to decreased model performance because of domain shift.…
CalFAT: Calibrated Federated Adversarial Training with Label Skewness
Chen Chen, Yuchen Liu, Xingjun Ma +1
Recent studies have shown that, like traditional machine learning, federated learning (FL) is also vulnerable to adversarial attacks. To improve the adversarial robustness of FL, f…