GAN-Based Data Augmentation with Vehicle Color Changes to Train a Vehicle Detection CNN
Object detection is a challenging task that requires a lot of labeled data to train convolutional neural networks (CNNs) that can achieve human-level accuracy. However, such data are not easy to obtain, as they involve significant manual work and costs to annotate the objects in images. Researchers...
Main Authors: | Aroona Ayub, HyungWon Kim |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2024-03-01
|
Series: | Electronics |
Subjects: | |
Online Access: | https://www.mdpi.com/2079-9292/13/7/1231 |
Similar Items
-
Collaborative Optimization of CNN and GAN for Bearing Fault Diagnosis under Unbalanced Datasets
by: Diwang Ruan, et al.
Published: (2021-10-01) -
CapGAN: Text-to-Image Synthesis Using Capsule GANs
by: Maryam Omar, et al.
Published: (2023-10-01) -
Data augmentation using conditional generative adversarial network (cGAN): Application for prediction of corrosion pit depth and testing using neural network
by: Haile Woldesellasse, et al.
Published: (2023-03-01) -
A Multi-Resolution Approach to GAN-Based Speech Enhancement
by: Hyung Yong Kim, et al.
Published: (2021-01-01) -
Augmentation leak-prevention scheme using an auxiliary classifier in GAN-based image generation
by: Jonghwa Shim, et al.
Published: (2023-09-01)