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Found 4 papers, 1 papers with code
Mitigating Gender Bias in Face Recognition Using the von Mises-Fisher Mixture Model
In spite of the high performance and reliability of deep learning algorithms in a wide range of everyday applications, many investigations tend to show that a lot of models exhibit biases, discriminating against specific subgroups of the population (e. g. gender, ethnicity).
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Face Verification
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One Picture is Worth a Thousand Words: A New Wallet Recovery Process
We introduce a new wallet recovery process.
Learning an Ethical Module for Bias Mitigation of pre-trained Models
In spite of the high performance and reliability of deep learning algorithms in broad range everyday applications, many investigations tend to show that a lot of models exhibit biases, discriminating against some subgroups of the population.
A Protection against the Extraction of Neural Network Models
Given oracle access to a Neural Network (NN), it is possible to extract its underlying model.
