Drop Loss for Person Attribute Recognition With Imbalanced Noisy-Labeled Samples.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on cybernetics"
DOI: 10.1109/tcyb.2022.3173356
Abstract: Person attribute recognition (PAR) aims to simultaneously predict multiple attributes of a person. Existing deep learning-based PAR methods have achieved impressive performance. Unfortunately, these methods usually ignore the fact that different attributes have an imbalance… read more here.
Keywords: loss; attribute recognition; drop loss; noisy labeled ... See more keywords
Distributionally Robust Federated Learning for Network Traffic Classification With Noisy Labels
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Mobile Computing"
DOI: 10.1109/tmc.2023.3319657
Abstract: Network traffic classifiers of mobile devices are widely learned with federated learning(FL) for privacy preservation. Noisy labels commonly occur in each device and deteriorate the accuracy of the learned network traffic classifier. Existing noise elimination… read more here.
Keywords: noisy; traffic data; network traffic; noisy labeled ... See more keywords