Object detection based on generative adversarial networks : unsupervised deep learning approaches물체 인식을 위한 적대적 생성 신경망 연구 : 딥러닝 기반의 비지도 학습법

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Recently, object detection has presented superior performance using the deep convolutional neural network (CNN). However, most CNN-based models need the bounding box information of the input image in pairs. To overcome this limitation, we propose the Generative Object Detection which learns with only cropped images that are not in pairs. Our model based on Generative Adversarial Networks (GAN) creates cropped images by making a mask that represents the object region. To achieve this goal, we devise a novel mask mean loss (MML) that helps the GAN be able to estimate the distribution of training data and uses dilated convolution for a wider reception eld in the generator. The experimental results show that Generative Object Detection improves mIoU and accuracy.
Advisors
Lee, Heung-Kyuresearcher이흥규researcher
Description
한국과학기술원 :전산학부,
Publisher
한국과학기술원
Issue Date
2020
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전산학부, 2020.2,[iv, 30 p. :]

Keywords

Object Detection▼aGenerative Adversarial Networks▼aDeep-learning▼aUnsupervised learning▼aComputer Vision; 물체인식▼a적대적 생성 신경망▼a딥러닝▼a비지도학습법▼a컴퓨터비전

URI
http://hdl.handle.net/10203/284677
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=911008&flag=dissertation
Appears in Collection
CS-Theses_Master(석사논문)
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