논문 리뷰15 [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. [X:AI] Seq2Seq 논문 리뷰 논문 원본 : https://arxiv.org/abs/1409.3215 Sequence to Sequence Learning with Neural NetworksDeep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. In this paparxiv.org RNN, LSTM,Seq2Seq 자료 AbstractDNN은 레이블이 지.. 2024. 2. 2. 이전 1 2 3 다음
