Á¤º¸°úÇÐȸ ÄÄÇ»ÆÃÀÇ ½ÇÁ¦ ³í¹®Áö (KIISE Transactions on Computing Practices)
Current Result Document :
ÇѱÛÁ¦¸ñ(Korean Title) |
Àΰø½Å°æ¸ÁÀÇ È¿À²Àû ³ëµå »çÈÄ °¡ÁöÄ¡±â |
¿µ¹®Á¦¸ñ(English Title) |
Efficient Node Posterior Pruning with Artificial Neural Networks |
ÀúÀÚ(Author) |
À±½ÂÇÑ
ÀÌÀ翵
ÀÌ¿õÈñ
±è¿µÈÆ
Seunghan Yoon
Jaeyoung Lee
Woonghee Lee
Younghoon Kim
|
¿ø¹®¼ö·Ïó(Citation) |
VOL 28 NO. 03 PP. 0184 ~ 0189 (2022. 03) |
Çѱ۳»¿ë (Korean Abstract) |
½ÉÃþ½Å°æ¸ÁÀº À̹ÌÁö ºÐ·ù ¹®Á¦¿¡¼ ³ôÀº Á¤È®µµ¸¦ º¸À̸ç ÁÖ¸ñÀ» ¹Þ¾Ò´Ù. ÇÏÁö¸¸ ºÐ·ùÇØ¾ß ÇÏ´Â ÇнÀµ¥ÀÌÅÍ¿Í Å¬·¡½º°¡ ¸¹¾ÆÁö°í º¹ÀâÇØÁö¸é ¸¹Àº ¸Å°³º¯¼ö¸¦ ÇÊ¿ä·Î Çϱ⠶§¹®¿¡ ³×Æ®¿öÅ©ÀÇ Å©±â°¡ Ä¿Áö°í ¿¬»ê·®µµ ¸¹¾ÆÁø´Ù. ±×·¡¼ ºÐ·ù ¼º´ÉÀº À¯ÁöÇÏ¸é¼ ³×Æ®¿öÅ©ÀÇ Å©±â¸¦ ÁÙÀÌ´Â ¿¬±¸°¡ ¸¹ÀÌ ÁøÇàµÇ°í ÀÖ´Ù. º» ¿¬±¸´Â À̹ÌÁö ºÐ·ù¸¦ ¼öÇàÇÏ´Â ½ÉÃþ½Å°æ¸ÁÀÇ ¿ÏÀü¿¬°á°èÃþ¿¡¼ °¡ÁöÄ¡±â¸¦ ¼öÇàÇÏ´Â ¹æ¹ýÀ» Á¦¾ÈÇÑ´Ù. ºÐ·ù¿¡ ¸¹Àº ¿µÇâÀ» ¹ÌÄ¡´Â ³ëµåÁýÇÕÀ» °ËÃâÇÏ¿© ±× ³ëµåµé¸¸À» Ãß·Ð ÀÛ¾÷¿¡ È°¿ëÇÑ´Ù. ÃßÃâµÈ Ư¡ÀÌ ¸ðÀÌ´Â Ãâ·Â°èÃþÀÇ Á÷Àü°èÃþµé¿¡¼ °¡ÁöÄ¡±â¸¦ ¼öÇàÇÏ°í ±âÁ¸ ¿¬±¸¿Í ºÐ·ù Á¤È®µµ¸¦ ºñ±³ÇÏ¿´´Ù. |
¿µ¹®³»¿ë (English Abstract) |
Deep neural networks have attracted attention with high accuracy in image classification problems. However, as the number of training data and classes to be classified increases and becomes more complex, the size of the network and the amount of computation increase because many parameters are required. Thus, many studies are being conducted to reduce the size of the network while maintaining the classification performance. This study proposes a method to perform pruning at the fully connected layer of deep neural networks that perform image classification. A set of nodes that have considerable influence on classification are detected, and only those nodes are used for inference. Pruning was performed in the layers immediately preceding the output layer where the extracted features were gathered, and the classification accuracy was compared with the previous studies. |
Å°¿öµå(Keyword) |
½ÉÃþ½Å°æ¸Á
¿ÏÀü¿¬°á°èÃþ
½Å°æ¸Á °¡ÁöÄ¡±â
À̹ÌÁö ºÐ·ù
½ÉÃþ½Å°æ¸Á
¿ÏÀü¿¬°á°èÃþ
½Å°æ¸Á °¡ÁöÄ¡±â
À̹ÌÁö ºÐ·ù
LightGBM
XAI
solar power generation
time series forecasting
|
ÆÄÀÏ÷ºÎ |
PDF ´Ù¿î·Îµå
|