Invention Grant
- Patent Title: Structure learning in convolutional neural networks
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Application No.: US17183021Application Date: 2021-02-23
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Publication No.: US11657286B2Publication Date: 2023-05-23
- Inventor: Andrew Rabinovich , Vijay Badrinarayanan , Daniel DeTone , Srivignesh Rajendran , Douglas Bertram Lee , Tomasz Malisiewicz
- Applicant: MAGIC LEAP, INC.
- Applicant Address: US FL Plantation
- Assignee: Magic Leap, Inc.
- Current Assignee: Magic Leap, Inc.
- Current Assignee Address: US FL Plantation
- Agency: Vista IP Law Group, LLP
- Main IPC: G06K9/00
- IPC: G06K9/00 ; G06V30/194 ; G06N3/082 ; G06V10/44 ; G06F18/24 ; G06F18/2413 ; G06N3/045

Abstract:
The present disclosure provides an improved approach to implement structure learning of neural networks by exploiting correlations in the data/problem the networks aim to solve. A greedy approach is described that finds bottlenecks of information gain from the bottom convolutional layers all the way to the fully connected layers. Rather than simply making the architecture deeper, additional computation and capacitance is only added where it is required.
Public/Granted literature
- US20210182636A1 STRUCTURE LEARNING IN CONVOLUTIONAL NEURAL NETWORKS Public/Granted day:2021-06-17
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