논문 리뷰17 [X:AI] EfficientNet 논문 리뷰 EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks논문 원본 https://arxiv.org/abs/1905.11946발표 영상 https://www.youtube.com/watch?v=BfqNoIeNzyg&t=601s발표 자료 (오타 ICLR 2019 -> ICML 2019) EfficientNet: Rethinking Model Scaling for Convolutional Neural NetworksConvolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accu.. 2024. 4. 3. [X:AI] U-Net 논문 리뷰 U-Net: Convolutional Networks for Biomedical Image Segmentation논문 원본 https://arxiv.org/abs/1505.04597발표 영상 https://www.youtube.com/watch?v=kX_qbaWNcEk&t=6s발표 자료 (오타 ICLR 2015 -> CVPR 2015) U-Net: Convolutional Networks for Biomedical Image SegmentationThere is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a ne.. 2024. 3. 27. [X:AI] ELMo 논문 리뷰 Deep contextualized word representations 논문 원본 https://arxiv.org/abs/1802.05365 Deep contextualized word representationsWe introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors arearxiv.org 발표 영상 http.. 2024. 3. 25. [X:AI] InceptionV2/3 논문 리뷰 Rethinking the Inception Architecture for Computer Vision논문 원본 https://arxiv.org/abs/1512.00567 Rethinking the Inception Architecture for Computer VisionConvolutional networks are at the core of most state-of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmarks. Al.. 2024. 3. 14. [X:AI] SPPNet 논문 리뷰 Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition논문 원본 https://arxiv.org/abs/1406.4729 Spatial Pyramid Pooling in Deep Convolutional Networks for Visual RecognitionExisting deep convolutional neural networks (CNNs) require a fixed-size (e.g., 224x224) input image. This requirement is "artificial" and may reduce the recognition accuracy for the images or sub-images of .. 2024. 3. 10. 이전 1 2 3 다음
