Àüü
ÀüÀÚ/Àü±â
Åë½Å
ÄÄÇ»ÅÍ
·Î±×ÀÎ
ȸ¿ø°¡ÀÔ
About Us
ÀÌ¿ë¾È³»
¿¬±¸¹®Çå
±¹³» ³í¹®Áö
¿µ¹® ³í¹®Áö
±¹³» ÇÐȸÁö
Çмú´ëȸ ÇÁ·Î½Ãµù
±¹³» ÇÐÀ§ ³í¹®
³í¹®Á¤º¸
¹é¼
±³À°Á¤º¸
¿¬±¸ ù°ÉÀ½
ÇаúÁ¤º¸
°ÀÇÁ¤º¸
µ¿¿µ»óÁ¤º¸
E-Learning
¿Â¶óÀÎ Àú³Î
½ÉÈÁ¤º¸
¿¬±¸ ¹× ±â¼úµ¿Çâ
Áֿ俬±¸ÅäÇÈ
ÁÖ¿ä°úÁ¦ ¹× ±â°ü
Çؿܱâ°ü °ü·ÃÀÚ·á
¹ÙÀÌ¿À Á¤º¸±â¼ú
ÁÖ¿ä Archive Site
Æ÷Ä¿½ºiN
¿¬±¸ÀÚ Á¤º¸
¶óÀÌ¡½ºÅ¸
ÆÄ¿öiNÅͺä
¼¼ÁßÇÑ
¿¬±¸ÀÚ·á
¹®ÀÚ DB
¿ë¾î»çÀü
¾Ë¸²¸¶´ç
ºÎ½Ç ÇмúÈ°µ¿ ¿¹¹æ
³í¹®¸ðÁý
´ëȸ¾È³»
What's New
¿¬±¸ºñÁ¤º¸
±¸ÀÎÁ¤º¸
°øÁö»çÇ×
CSERIC ±¤Àå
Post-Conference
¿¬±¸ÀÚ Ä«Æä
ÀÚÀ¯°Ô½ÃÆÇ
Q&A
´Ý±â
»çÀÌÆ®¸Ê
¿¬±¸¹®Çå
±¹³» ³í¹®Áö
¿µ¹® ³í¹®Áö
±¹³» ÇÐȸÁö
Çмú´ëȸ ÇÁ·Î½Ãµù
±¹³» ÇÐÀ§ ³í¹®
³í¹®Á¤º¸
¹é¼
±³À°Á¤º¸
¿¬±¸ ù°ÉÀ½
ÇаúÁ¤º¸
°ÀÇÁ¤º¸
µ¿¿µ»óÁ¤º¸
E-Learning
¿Â¶óÀÎ Àú³Î
½ÉÈÁ¤º¸
¿¬±¸ ¹× ±â¼úµ¿Çâ
Áֿ俬±¸ÅäÇÈ
ÁÖ¿ä°úÁ¦ ¹× ±â°ü
Çؿܱâ°ü °ü·ÃÀÚ·á
¹ÙÀÌ¿À Á¤º¸±â¼ú
ÁÖ¿ä Archive Site
ÄÄÇ»ÅÍiN
¿¬±¸ÀÚ Á¤º¸
¿¬±¸ÀÚ·á
¹®ÀÚ DB
Ȧ·Î±×·¥ DB
¿ë¾î»çÀü
¾Ë¸²¸¶´ç
ºÎ½Ç ÇмúÈ°µ¿ ¿¹¹æ
³í¹®¸ðÁý
´ëȸ¾È³»
What's New
¿¬±¸ºñ Á¤º¸
±¸ÀÎÁ¤º¸
°øÁö»çÇ×
IT Daily
CSERIC ±¤Àå
Post-Conference
¿¬±¸ÀÚ Ä«Æä
ÀÚÀ¯°Ô½ÃÆÇ
Q&A
¼ºñ½º ¹Ù·Î°¡±â
¼³¹®Á¶»ç
¿¬±¸À±¸®
°ü·Ã±â°ü
Please wait....
¿¬±¸¹®Çå
±¹³» ³í¹®Áö
¿µ¹® ³í¹®Áö
±¹³» ÇÐȸÁö
Çмú´ëȸ ÇÁ·Î½Ãµù
±¹³» ÇÐÀ§ ³í¹®
³í¹®Á¤º¸
¹é¼
±¹³» ³í¹®Áö
Ȩ > ¿¬±¸¹®Çå > ±¹³» ³í¹®Áö >
Çѱ¹Á¤º¸Ã³¸®ÇÐȸ ³í¹®Áö
>
Á¤º¸Ã³¸®ÇÐȸ ³í¹®Áö ¼ÒÇÁÆ®¿þ¾î ¹× µ¥ÀÌÅÍ °øÇÐ
Á¤º¸Ã³¸®ÇÐȸ ³í¹®Áö ¼ÒÇÁÆ®¿þ¾î ¹× µ¥ÀÌÅÍ °øÇÐ
Current Result Document :
ÇѱÛÁ¦¸ñ(Korean Title)
eGAN ¸ðµ¨ÀÇ ¼º´É°³¼±À» À§ÇÑ ¿¡Áö °ËÃâ ±â¹ý
¿µ¹®Á¦¸ñ(English Title)
An Edge Detection Technique for Performance Improvement of eGAN
ÀúÀÚ(Author)
¹Ú´ë°æ
·ù°æÁØ
½Åµ¿ÀÏ
½Åµ¿±Ô
¹ÚÁ¤Âù
±èÁø±¹
Park DaeKyeong
Ryu KyungJoon
Shin DongIl
Shin DongKyoo
Park JeongChan
Kim JinGoog
±â¹Î¼Û
ÃÖ¿µ¿ì
Min Song Ki
Yeong Woo Choi
ÀÌÃÊ¿¬
¹ÚÁö¼ö
¼ÕÁø°ï
Lee Cho Youn
Ji Su Park
Jin Gon Shon
¿ø¹®¼ö·Ïó(Citation)
VOL 10 NO. 03 PP. 0109 ~ 0114 (2021. 03)
Çѱ۳»¿ë
(Korean Abstract)
GAN(Generative Adversarial Network, »ý¼ºÀû Àû´ë ½Å°æ¸Á)Àº À̹ÌÁö »ý¼º¸ðµ¨·Î¼ »ý¼º±â ³×Æ®¿öÅ©¿Í ÆǺ°±â ³×Æ®¿öÅ©·Î ±¸¼ºµÇ¸ç ½ÇÁ¦ °°Àº À̹ÌÁö¸¦ »ý¼ºÇÑ´Ù. GAN¿¡ ÀÇÇØ »ý¼ºµÈ À̹ÌÁö´Â ½ÇÁ¦ À̹ÌÁö¿Í À¯»çÇØ¾ß ÇϹǷΠ»ý¼ºµÈ À̹ÌÁö¿Í ½ÇÁ¦ À̹ÌÁöÀÇ ¼Õ½Ç ¿ÀÂ÷¸¦ ÃÖ¼ÒÈÇÏ´Â ¼Õ½ÇÇÔ¼ö (loss function)¸¦ »ç¿ëÇÑ´Ù. ±×·¯³ª GANÀÇ ¼Õ½ÇÇÔ¼ö´Â À̹ÌÁö¸¦ »ý¼ºÇÏ´Â ÇнÀÀ» ºÒ¾ÈÁ¤ÇÏ°Ô ¸¸µé¾î À̹ÌÁöÀÇ Ç°ÁúÀ» ¶³¾î¶ß¸°´Ù´Â ¹®Á¦Á¡ÀÌ ÀÖ´Ù. ÀÌ·¯ÇÑ ¹®Á¦¸¦ ÇØ°áÇϱâ À§ÇØ º» ³í¹®¿¡¼´Â GAN °ü·Ã ¿¬±¸¸¦ ºÐ¼®ÇÏ°í ¿¡Áö °ËÃâ(edge detection)À» ÀÌ¿ëÇÑ eGAN(edge GAN)À» Á¦¾ÈÇÑ´Ù. ½ÇÇè °á°ú eGAN ¸ðµ¨ÀÌ ±âÁ¸ÀÇ GAN ¸ðµ¨º¸´Ù ¼º´ÉÀÌ °³¼±µÇ¾ú´Ù.
¿µ¹®³»¿ë
(English Abstract)
GAN(Generative Adversarial Network) is an image generation model, which is composed of a generator network and a discriminator network, and generates an image similar to a real image. Since the image generated by the GAN should be similar to the actual image, a loss function is used to minimize the loss error of the generated image. However, there is a problem that the loss function of GAN degrades the quality of the image by making the learning to generate the image unstable. To solve this problem, this paper analyzes GAN-related studies and proposes an edge GAN(eGAN) using edge detection. As a result of the experiment, the eGAN model has improved performance over the existing GAN model.
Å°¿öµå(Keyword)
±â°èÇнÀ
È£½ºÆ® ±â¹Ý ħÀÔ Å½Áö ½Ã½ºÅÛ
³×Æ®¿öÅ© ±â¹Ý ħÀÔ Å½Áö ½Ã½ºÅÛ
LID-DS
Machine Learning
HIDS
NIDS
¾ó±¼ ÀÎÁõ
Àû¿Ü¼± À̹ÌÁö
¸ÖƼ ¼Æ÷Æ® º¤ÅÍ ¸Ó½Å
Á¤¸é±¤ ³ëÃâ
Face Identification
Near-infrared Image
Multi Support Vector Machine (Multi-SVM)
Light Overexposure
»ý¼ºÀû Àû´ë ½Å°æ¸Á
¼Õ½Ç ÇÔ¼ö
¿¡Áö °ËÃâ
eGAN
Generative Adversarial Network
Loss Function
Edge Detection
eGAN
ÆÄÀÏ÷ºÎ
PDF ´Ù¿î·Îµå
¸ñ·Ï
Copyright(c)
Computer Science Engineering Research Information Center
. All rights reserved.