딥러닝 논문 리뷰
1.(논문 리뷰, 코드 구현) ImageNet Classification with Deep Convolutional Neural Networks (2012)
AbstractThe neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by
2.(논문 리뷰, 코드) VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION(2015)
VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION(큰규격이미지 인식을 위한 매우 깊은 컨벌루젼 네트워크)1\. very small (3x3) convolution filters의 구조를 사용하여 깊이
3.딥러닝을 위한 논문 가이드
인공지능 분야를 위한 논문을 접근하는 가이드는 "앤드류 응 교수님의 ML/DL 커리어 경력과 논문 읽기 관련된 조언을 기반"을 기초로 진행함.https://media-ai.tistory.com/7https://www.youtube.com/watch?v
4.(논문 리뷰, 코드) Going deeper with convolutions (2014)
논문 원본 : https://arxiv.org/pdf/1409.4842.pdfTitle : Going deeper with convolutions(합성곱으로 더욱더 깊게 가자!)Abstract 1\. 'Inception'이라는 convolutional
5.(논문 리뷰)Rich feature hierarchies for accurate object detection and semantic segmentation Tech report (v5) (2014)
논문 : https://arxiv.org/pdf/1311.2524
6.(논문리뷰) YOLO9000: Better, Faster, Stronger (2016)
https://arxiv.org/pdf/1612.08242 Abstract We introduce YOLO9000, a state-of-the-art, real-time object detection system that can detect over 9000 obje