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Style Feature Extraction Using Contrastive Conditioned Variational Autoencoders with Mutual Information Constraints
In this paper, we propose a CVAE-based method that extracts style features using only unlabeled data.
Selective Forgetting of Deep Networks at a Finer Level than Samples
Such forgetting is crucial also in a practical sense since the deployed DNNs may be trained on the data with outliers, poisoned by attackers, or with leaked/sensitive information.
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