Invention Grant
- Patent Title: Systems and methods for training neural networks for regression without ground truth training samples
-
Application No.: US15807401Application Date: 2017-11-08
-
Publication No.: US10565686B2Publication Date: 2020-02-18
- Inventor: Jaakko T. Lehtinen , Timo Oskari Aila , Jon Niklas Theodor Hasselgren , Carl Jacob Munkberg
- Applicant: NVIDIA Corporation
- Applicant Address: US CA Santa Clara
- Assignee: NVIDIA Corporation
- Current Assignee: NVIDIA Corporation
- Current Assignee Address: US CA Santa Clara
- Agency: Leydig, Voit & Mayer, Ltd.
- Main IPC: G06T5/00
- IPC: G06T5/00 ; G06N3/08 ; G06N3/04

Abstract:
A method, computer readable medium, and system are disclosed for training a neural network. The method includes the steps of selecting an input sample from a set of training data that includes input samples and noisy target samples, where the input samples and the noisy target samples each correspond to a latent, clean target sample. The input sample is processed by a neural network model to produce an output and a noisy target sample is selected from the set of training data, where the noisy target samples have a distribution relative to the latent, clean target sample. The method also includes adjusting parameter values of the neural network model to reduce differences between the output and the noisy target sample.
Public/Granted literature
- US20180357753A1 SYSTEMS AND METHODS FOR TRAINING NEURAL NETWORKS FOR REGRESSION WITHOUT GROUND TRUTH TRAINING SAMPLES Public/Granted day:2018-12-13
Information query