[4]He, Kaiming, et al. "Deep residual learning for image recognition." arXiv preprint arXiv:1512.03385 (2015). [5]He, Kaiming, and Jian Sun. "Convolutional neural networks at constrained time cost." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015. [6]Mo...
论文代码:https://github.com/KaimingHe/deep-residual-networks ResNet(Residual Neural Network)由微软研究院的Kaiming He等4名华人提出,通过使用Residual Unit成功训练152层深的神经网络,在ILSVRC 2015比赛中获得了冠军,取得3.57%的top-5错误率,同时参数量却比VGGNet低,效果非常突出。 核心内容: ResNet...
[[12] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[C]// International Conference on Neural Information Processing Systems. Curran Associates Inc. 2012:1097-1105.] [[13] He K, Zhang X, Ren S, et al. Deep Residual Learning for Image...
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...
Aggregated Residual Transformations for Deep Neural Networks Abstract 我们提出了一个简单的、高度模块化的图像分类网络架构。我们的网络是通过重复一个构建块来构建的,该构建块聚合了一组具有相同拓扑结构的转换(transformations)。我们的简单的设计得到一个均匀的多分支结构,只有设置了少数的超参数。这种策略使一个新的...
2.2 Spiking Residual Structure 以前的ANN2SNN方法注意到了纯前馈ANN和残差ANN之间的区别,并对转换进行了特定的归一化。Hu等人[17]是第一个将ANN2SNN中的残差结构与SNN中的缩放捷径相结合,以匹配原始ANN的激活。Sengupta等人[49]提出了Spike-Norm来平衡SNN的阈值,并通过将VGG和ResNet转换为SNN来验证其方法。现有的...
当前主流的DL模型包括卷积神经网络(convolutional neural network,CNN)、循环神经网络(recurrent neural network,RNN)和图神经网络(graph neural network,GNN)等,以及这些模型的变体,如残差卷积网络模型(deep residual network,ResNet)、变分自...
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...
A combination of support vector machines, sparse-coding methods, and hand-coded feature extractors with fully convolutional neural networks (FCNN) and deep residual networks into ensembles was evaluated. The experimental results emphasized that the integrated multitude of machine-learning methods achieved ...
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...