Earticle

초록

영어

While recent convolutional neural networks (CNNs) for object detection have been substantially improved, they require a large amount of annotated data to further improve their accuracy to the level of human. Such annotated data is scarce. The generation of ground truth to annotate training data is a time consuming and resource expensive process. Researchers use traditional data augmentation techniques to increase the amount of training data. Recently, generative models are being employed to augment data which produces diverse training data. This leads to an increase in model performance. This paper presents a method to train a GAN network and generate augmented data of any domain of interest with the least compromise in the quality of generated images. The proposed method trains a GAN with vehicles images of different colors. Then it can change the color of vehicles in any given vehicle dataset to a set of specified colors.

목차

Abstract
I. INTRODUCTION
II. PROPOSED METHOD
A. Training Dataset
B. Training GAN Network
III. EXPERIMENTS AND RESULTS
IV. CONCLUSION
REFERENCES

키워드

저자

• Aroona Ayub [ Electronics Engineering Department, School of Electronics Engineering Chungbuk National University ]
• HyungWon Kim [ Electronics Engineering Department, School of Electronics Engineering Chungbuk National University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004