Learning Realistic Patterns from Unrealistic Stimuli: Generalization and Data Anonymization

A simple yet unconventional approach for anonymized data synthesis to enable third parties to benefit from such private data to explore the feasibility of learning implicitly from unrealistic, task-relevant stimuli, which are synthesized by exciting the neurons of a trained deep neural network (DNN).