https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf [2] Szegedy et al, “Going deeper with convolutions”, CVPR(2015) 1409.4842 (arxiv.org) [3] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Deep Residual Learning for Image Recognition, ...
然而,EfficientNet 提出了一种新的复合缩放方法,它同时在深度、宽度和分辨率三个维度上进行调整: EfficientNet 的基础网络结构基于 MobileNetV2 中的倒残差块(Inverted Residual Blocks)和 Squeeze-and-Excitation(SE)模块。具体来说: MBConv(Mobile Inverted Bottleneck Convolution):每个 MBConv 包含一个深度可分离卷积(De...
论文: FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics 论文地址:https://arxiv.org/pdf/1612.05360 论文思想: FusionNet利用机器学习的最新进展,如语义分割(U-Net)和残差神经网络,新引入了基于累加的跳过连接,允许更深入的网络体系结构来实现更精确的分割。 论文...
树卷积神经网络Tree-CNN: A Deep Convolutional Neural Network for Lifelong Learning,程序员大本营,技术文章内容聚合第一站。
Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of le...
3. Deep Residual Learning 深度残差学习 3.1. Residual Learning 残差学习 3.2 Identity Mapping by Shortcuts 快捷恒等映射 3.3 Network Architectures 网络架构 3.4. Implementation 实现 4. Experiments 实验 4.1. ImageNet Classification ImageNet图像分类
提出了一种用于快速多尺度目标检测的统一深度神经网络,即多尺度CNN (MS-CNN)。MS-CNN由建议子网络和检测子网络组成。在建议子网中,在多个输出层进行检测,使感受野匹配不同尺度的对象。这些互补的尺度特异性探测器被结合起来产生一个强大的多尺度目标探测器。通过优化多任务损失,实现了统一网络的端到端学习。此外,还...
树卷积神经网络Tree-CNN: A Deep Convolutional Neural Network for Lifelong Learning 2018-04-17 08:32:39 看_这是一群菜鸟 阅读数 1906 收藏 更多 分类专栏: 论文解读
论文阅读——DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation,程序员大本营,技术文章内容聚合第一站。
residual dense block (RDB)63, which is shown in Fig.8b. The original GRDN architecture can be conceptually divided into three parts. The first part consists of a convolutional layer followed by a downsampling layer based on a convolutional stride, the middle part is built by cascading GRDBs ...