Invention Application
- Patent Title: HIERARCHICAL SUPERVISED TRAINING FOR NEURAL NETWORKS
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Application No.: PCT/US2022/073173Application Date: 2022-06-25
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Publication No.: WO2022272311A1Publication Date: 2022-12-29
- Inventor: BORSE, Shubhankar Mangesh , CAI, Hong , ZHANG, Yizhe , PORIKLI, Fatih Murat
- Applicant: QUALCOMM INCORPORATED
- Applicant Address: ATTN: International IP Administration
- Assignee: QUALCOMM INCORPORATED
- Current Assignee: QUALCOMM INCORPORATED
- Current Assignee Address: ATTN: International IP Administration
- Agency: ROBERTS, Steven E. et al.
- Priority: US17/808,949 2022-06-24
- Main IPC: G06N3/04
- IPC: G06N3/04 ; G06N3/08 ; G06F18/23 ; G06F18/2431 ; G06N3/045 ; G06N3/082
Abstract:
Certain aspects of the present disclosure provide techniques for training neural networks using hierarchical supervision. An example method generally includes training a neural network with a plurality of stages using a training data set and an initial number of classification clusters into which data in the training data set can be classified. A cluster-validation set performance metric is generated for each stage based on a reduced number of classification clusters relative to the initial number of classification clusters and a validation data set. A number of classification clusters to implement at each stage is selected based on the cluster-validation set performance metric and an angle selected relative to the cluster-validation set performance metric for a last stage of the neural network. The neural network is retrained based on the training data set and the selected number of classification clusters for each stage, and the trained neural network is deployed.
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