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Found 5 papers, 1 papers with code
Brain-inspired Distributed Memorization Learning for Efficient Feature-free Unsupervised Domain Adaptation
Compared with gradient based artificial neural networks, biological neural networks usually show a more powerful generalization ability to quickly adapt to unknown environments without using any gradient back-propagation procedure.
Meta Balanced Network for Fair Face Recognition
Finally, to mitigate the algorithmic bias, we propose a novel meta-learning algorithm, called Meta Balanced Network (MBN), which learns adaptive margins in large margin loss such that the model optimized by this loss can perform fairly across people with different skin tones.
Ada-NETS: Face Clustering via Adaptive Neighbour Discovery in the Structure Space
In Ada-NETS, each face is transformed to a new structure space, obtaining robust features by considering face features of the neighbour images.
Adaptive Label Noise Cleaning With Meta-Supervision for Deep Face Recognition
The training of a deep face recognition system usually faces the interference of label noise in the training data.
Global-Local GCN: Large-Scale Label Noise Cleansing for Face Recognition
Extensive experiments show that cleansing widely used datasets, such as CASIA-WebFace, VGGFace2, MegaFace2, and MS-Celeb-1M, using the proposed method can improve the recognition performance of state-of-the-art representation learning methods like Arcface.
