Computer Vision3 (작성 중...)[JIS] Deep Learning for Image Classification [JIS] Journal Introduction Summary: Deep Learning for Image Classification [Since 2017] 1. [EfficientNet] Tan, Mingxing, and Quoc Le. "Efficientnet: Rethinking model scaling for convolutional neural networks." International conference on machine learning. PMLR, 2019. 2. [NasNet] Zoph, Barret, et al. "Learning transferable architectures for scalable image recognition." Proceedings of the IEEE con.. 2023. 12. 26. [DenseNet] Huang et al., 2017, Densely Connected Convolutional Networks # 세줄 요약 # We introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. For each layer, the feature-maps of all preceding layers are used as inputs, and their own feature-maps are used as inputs into all subsequent layers. DenseNet has several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feat.. 2022. 3. 13. [GoogLeNet] Szegedy et al., 2015, Going Deeper with Convolutions # 세줄요약 # 'Inception'이라고 불리는 새로운 모듈로 구성한 Deep CNN(Convolutional Neural Network), 일명 'GoogLeNet' 을 처음으로 소개한 논문으로, GoogleNet은 ILSVRC14(ImageNet Large-Scale Visual Recoginition Challenge 2014)에서 Classification과 Detection 모두에서 최고의 성능을 보였다. Inception 모듈은 multi-scale processing과 Hebbian principle에서 영감을 얻어 이전 층의 특성지도(feature map)를 다양한 크기의 필터(1x1, 3x3, 5x5, pooling)로 병렬처리한 이후 다시 하나의 출력으로 합치는 구조를 가지고 있다. .. 2020. 12. 16. 이전 1 다음
