参考文献 [1] Mishra, A., Nurvitadhi, E., Cook, J. J., and Marr, D. Wrpn: Wide reduced-precision networks. In International Conference on Learning Representations (ICLR), 2018 [2] Guo, Nianhui, et al. "Boolnet: minimizing the energy consumption of binary neural networks." arXiv pre...
2017 年 2 月,已经加入 Facebook 的何恺明和 S. Xie 等人在《残差变换聚合深度网络》(Aggregated Residual Transformations for Deep Neural Networks)[8]中提出一个名为 ResNeXt 的残差网络变体,它的构建块如下所示: 左:[2]的构建块;右:ResNeXt 的一个构建块,基数=32 这个可能看起来很眼熟,因为它与 GoogLeNet...
Imagenet classification with deep convolutional neural networks[C]//Advances in neural information processing systems. 2012: 1097-1105. 权重初始化和Momentum优化方法的研究Sutskever I, Martens J, Dahl G, et al. On the importance of initialization and momentum in deep learning[C]//International ...
基于深度残差网络优化的残差网络变体:Pre-ResNet、加权残差网络(weighted residual network,WResNet)、金字塔残差网络(pyramidal residual network,PyramidalNet)、多级残差卷积神经网络(residual networks of residual networks,RoR)、金字塔多级残差卷积神经网络(pyramidal RoR,PRoR)等; ...
对于深度网络快速适应(Fast Adaptation of Deep Networks)的模型未知元学习(Model-Agnostic Meta-Learning) (https://arxiv.org/pdf/1703.03400v3.pdf) 基于记忆增强神经网络学习(Memory-Augmented Neural Networks)的One-Shot Learning (https://arxiv.org/pdf/1605.06065v1.pdf) ...
2017 年 2 月,已经加入 Facebook 的何恺明和 S. Xie 等人在《残差变换聚合深度网络》(Aggregated Residual Transformations for Deep Neural Networks)[8]中提出一个名为 ResNeXt 的残差网络变体,它的构建块如下所示: 左:[2]的构建块;右:ResNeXt 的一个构建块,基数=32 ...
We believe that it will be of much interest to a large group of neural engineers and neuroscientists. What has the reaction been to your article so far, and what impact would you like your article to have? We think our article is excellent, it has attracted the attention of a lot of ...
为此,我们提出了一个基于自适应混合金字塔的局部修图框架(ABPN: Adaptive Blend Pyramid Network for Real-Time Local Retouching of Ultra High-Resolution Photo, CVPR2022,[27]),以实现超高分辨率图像的精细化局部修图,下面我们对其实现细节进行介绍。 3.1 网络整体结构 ...
[2] Guo, Nianhui, et al. "Boolnet: minimizing the energy consumption of binary neural networks." arXiv preprint arXiv:2106.06991 (2021).
——列举在ImageNet数据集上提高分类性能的训练技巧 ——以ResNet-50为例,将top-1准确率从75.3%提高的79.29% Baseline 训练 1、随机采样...