Invention Grant
- Patent Title: Encoding and reconstructing inputs using neural networks
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Application No.: US16323205Application Date: 2017-08-03
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Publication No.: US11308385B2Publication Date: 2022-04-19
- Inventor: Martin Abadi , David Godbe Andersen
- Applicant: Google LLC
- Applicant Address: US CA Mountain View
- Assignee: Google LLC
- Current Assignee: Google LLC
- Current Assignee Address: US CA Mountain View
- Agency: Fish & Richardson P.C.
- International Application: PCT/US2017/045329 WO 20170803
- International Announcement: WO2018/027050 WO 20180208
- Main IPC: G06N3/04
- IPC: G06N3/04 ; G06N3/08

Abstract:
Systems, methods, devices, and other techniques are described herein for training and using neural networks to encode inputs and to process encoded inputs, e.g., to reconstruct inputs from the encoded inputs. A neural network system can include an encoder neural network, a trusted decoder neural network, and an adversary decoder neural network. The encoder neural network processes a primary neural network input and a key input to generate an encoded representation of the primary neural network input. The trusted decoder neural network processes the encoded representation and the key input to generate a first estimated reconstruction of the primary neural network input. The adversary decoder neural network processes the encoded representation without the key input to generate a second estimated reconstruction of the primary neural network input. The encoder and trusted decoder neural networks can be trained jointly, and these networks trained adversarially to the adversary decoder neural network.
Public/Granted literature
- US20190171929A1 ENCODING AND RECONSTRUCTING INPUTS USING NEURAL NETWORKS Public/Granted day:2019-06-06
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