Understanding the Fragility of Neural Network Representations for MCM

  • Gerg, Isaac D. (PI)

Project: Research project

Project Details


In this effort, PSU/ARL proposes to begin modeling the representation produced by deep neural networks (NNs) with the goal of understanding their fragility. NN algorithms are sensitive inthree ways:1. Input information quality (i.e. sensor fidelity, simulation fidelity, label quality, intersensormismatch in the case of transfer learning, etc)2. Model training approach (i.e. stopping criteria, optimization algorithm, etc)3. Model architecture (i.e. Alexnet, ResNet, Densenet, NASNet, Random, etc.)PSU/ARL wants to model the NN performance similar to work done in performance estimation, but with the explicit goals of being able to: characterize weaknesses in what the network models describe the weakness of the modeling in a practical manner mitigate the weaknesses as best as possible

Effective start/end date5/18/20 → …


  • U.S. Navy: $350,000.00


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