Invention Grant
- Patent Title: Adversarial training of neural networks using information about activation path differentials
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Application No.: US16361397Application Date: 2019-03-22
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Publication No.: US11657162B2Publication Date: 2023-05-23
- Inventor: Michael Kounavis , Antonios Papadimitriou , Anindya Sankar Paul , Micah Sheller , Li Chen , Cory Cornelius , Brandon Edwards
- Applicant: Intel Corporation
- Applicant Address: US CA Santa Clara
- Assignee: INTEL CORPORATION
- Current Assignee: INTEL CORPORATION
- Current Assignee Address: US CA Santa Clara
- Agency: Jaffery Watson Mendonsa & Hamilton LLP
- Main IPC: G06F21/60
- IPC: G06F21/60 ; G06N3/08 ; G06N3/04 ; G06F21/52

Abstract:
In one example an apparatus comprises a memory and a processor to create, from a first deep neural network (DNN) model, a first plurality of DNN models, generate a first set of adversarial examples that are misclassified by the first plurality of deep neural network (DNN) models, determine a first set of activation path differentials between the first plurality of adversarial examples, generate, from the first set of activation path differentials, at least one composite adversarial example which incorporates at least one intersecting critical path that is shared between at least two adversarial examples in the first set of adversarial examples, and use the at least one composite adversarial example to generate a set of inputs for a subsequent training iteration of the DNN model. Other examples may be described.
Public/Granted literature
- US20190220605A1 ADVERSARIAL TRAINING OF NEURAL NETWORKS USING INFORMATION ABOUT ACTIVATION PATH DIFFERENTIALS Public/Granted day:2019-07-18
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