Invention Grant
- Patent Title: Differentiable jaccard loss approximation for training an artificial neural network
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Application No.: US15889440Application Date: 2018-02-06
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Publication No.: US10535141B2Publication Date: 2020-01-14
- Inventor: Yair Movshovitz-Attias , Elad Edwin Tzvi Eban
- Applicant: Google LLC
- Applicant Address: US CA Mountain View
- Assignee: Google LLC
- Current Assignee: Google LLC
- Current Assignee Address: US CA Mountain View
- Agency: McDonnell Boehnen Hulbert & Berghoff LLP
- Main IPC: G06T7/11
- IPC: G06T7/11 ; G06T7/136 ; G06N3/08 ; G06N3/04

Abstract:
Systems and methods described herein may relate to training an artificial neural network (ANN) using a differentiable Jaccard Loss approximation. An example embodiment may involve obtaining a training image and a corresponding ground truth mask that represents a desired segmentation of the training image. The embodiment may further involve applying an ANN on the training image to generate an output segmentation of the training image that depends on a plurality of weights of the ANN and determining a differentiable Jaccard Loss approximation based on the output segmentation of the training image and the ground truth mask. The embodiment also involves modifying one or more weights of the ANN based on the differentiable Jaccard Loss approximation and providing a representation of the ANN as modified to a mobile computing device.
Public/Granted literature
- US20190244362A1 Differentiable Jaccard Loss Approximation for Training an Artificial Neural Network Public/Granted day:2019-08-08
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