Invention Application
- Patent Title: NEURAL ARCHITECTURE SEARCH FOR DENSE IMAGE PREDICTION TASKS
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Application No.: US17107745Application Date: 2020-11-30
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Publication No.: US20210081796A1Publication Date: 2021-03-18
- Inventor: Barret Zoph , Jonathon Shlens , Yukun Zhu , Maxwell Donald Collins , Liang-Chieh Chen , Gerhard Florian Schroff , Hartwig Adam , Georgios Papandreou
- Applicant: Google LLC
- Applicant Address: US CA Mountain View
- Assignee: Google LLC
- Current Assignee: Google LLC
- Current Assignee Address: US CA Mountain View
- Priority: GR20180100232 20180529
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G06N20/00 ; G06F17/15 ; G06K9/62 ; G06N3/04

Abstract:
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining neural network architectures. One of the methods includes obtaining training data for a dense image prediction task; and determining an architecture for a neural network configured to perform the dense image prediction task, comprising: searching a space of candidate architectures to identify one or more best performing architectures using the training data, wherein each candidate architecture in the space of candidate architectures comprises (i) the same first neural network backbone that is configured to receive an input image and to process the input image to generate a plurality of feature maps and (ii) a different dense prediction cell configured to process the plurality of feature maps and to generate an output for the dense image prediction task; and determining the architecture for the neural network based on the best performing candidate architectures.
Information query