Hybrid synthetic data generation pipeline that outperforms real data

Hybrid synthetic data generation pipeline that outperforms real data

Fine-tuning a pretrained model with real data for a machine learning task requires many hours of manual work, especially for computer vision tasks, where collection and annotation of data can be very time-consuming. We present a framework and methodology for synthetic data collection that is not onl...

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Bibliographic Details
Main Authors: Madden, M.G (Author), Natarajan, S.A (Author)
Format: Article
Language:English
Published: SPIE 2023
Subjects:
Benchmarking
Classification (of information)
computer vision
Computer vision
deep learning
Deep learning
Fine tuning
image classification
Image classification
Images classification
Learning algorithms
learning rate
Learning rates
Learning tasks
Machine-learning
Objectnet
ObjectNet
Open source software
Open systems
State of the art
Statistical tests
synthetic data
Synthetic data
Synthetic data generations
Online Access:View Fulltext in Publisher
View in Scopus

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