公开(公告)号：US20220122001A1

公开(公告)日：2022-04-21

申请号：US17219350

申请日：2021-03-31

Applicant: Nvidia Corporation

Inventor： Tae Eun Choe ,  Aman Kishore ,  Junghyun Kwon ,  Minwoo Park ,  Pengfei Hao ,  Akshita Mittel

IPC: G06N20/20 ,  A63F13/50 ,  G06N3/04

Abstract: Approaches presented herein provide for the generation of synthetic data to fortify a dataset for use in training a network via imitation learning. In at least one embodiment, a system is evaluated to identify failure cases, such as may correspond to false positives and false negative detections. Additional synthetic data imitating these failure cases can then be generated and utilized to provide a more abundant dataset. A network or model can then be trained, or retrained, with the original training data and the additional synthetic data. In one or more embodiments, these steps may be repeated until the evaluation metric converges, with additional synthetic training data being generated corresponding to the failure cases at each training pass.