Sep 21, 2020 · This work investigates a simple yet unconventional approach for anonymized data synthesis to enable third parties to benefit from such private data.
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Jan 4, 2022 · We explore the feasibility of learning implicitly from visually unrealistic, task-relevant stimuli, which are synthesized by exciting the ...
This work investigates a simple yet unconventional approach for anonymized data synthesis to enable third parties to benefit from such anonymized data. We ...
A possible solution to this problem is to release an anonymized version of the data with the use of existing database anonymization strategies like k-anonymity ...
Return to Article Details Learning Realistic Patterns from Visually Unrealistic Stimuli: Generalization and Data Anonymization Download Download PDF.
This work explores the feasibility of learning implicitly from visually unrealistic, task-relevant stimuli, which are synthesized by exciting the neurons of ...
We explore the feasibility of learning implicitly from unrealistic, task-relevant stimuli, which are synthesized by exciting the neurons of a trained deep ...
Stimuli: Generalization and Data Anonymization. Konstantinos Nikolaidis ... We want hS to learn indirectly through the stimuli and generalize on the real data.
This work investigates a simple yet unconventional approach for anonymized data synthesis to enable third parties to benefit from such anonymized data. We ...
Learning Realistic Patterns from Visually Unrealistic Stimuli: Generalization and Data Anonymization (Extended Abstract). Proceedings of the Thirty-First ...