这再次表明cardinality比深度和宽度维度更有效。 Residual connections. 下表显示了残差(shortcut)连接的效果: 从ResNeXt-50删除shortcuts会使错误增加3.9个百分点,达到26.1%。而从ResNet-50中删除shortcuts则要糟糕得多(31.2%)。这些比较表明残差连接有助于优化,而聚合转换是更强的表示,这一点可以从以下事实看出:它们...
Drawbacks of Spiking ResNet 1.Spiking ResNet并不适用于所有神经元模型来实现identity mapping。 如果添加网络层实现了identity mapping,深度模型的训练误差不会大于浅层模型。但是最初单纯地增加层数无法实现这一要求,直到residual learning的提出。下面是三种不同的残差块(包括本文提出的SEW)的示意图: 图a和图b要实...
Deep Residual Networks学习(一) 炼丹师 《Deep Residual Learning in Spiking Neural Networks》笔记 论文传送门: 2102.04159v3.pdf (arxiv.org)Abstract现有的Spiking ResNet都是参照ANN中的标准残差块,简单地把ReLu激活函数层换成spiking neurons,所以说会发生degradation的问题(深网络… weili...发表于SNN 【论文...
ResNeXt(ILSVRC 2016第二) 新的维度Cardinality(基数),和宽度、深度并列的第三维 1 动机 1.1 第三维:Cardinality 提高精确度的两种方法——加深或加宽,都会导致参数量增加 提出第三维Cardinality,在限制参数量情况下增加精度 2 方法 2.1 堆叠网络 2.1.1 借鉴VGG思想,堆叠重复结构加深网络 2.1.2 two rules: 产生相...
我们可以将其看做如下“分离-转换-合并”过程,如图2: 1)分离:将向量x嵌入较低的维度,得到各个分量的x_i; 2)转换:在低维空间中分别对x_i进行变换,对于上述内积,其变换为w_i*x_i; 3)合并:将所有低维空间的分量聚集在一起,对上述内积为直接求和。
Despite the practical success of deep neural networks, a comprehensive theoretical framework that can predict practically relevant scores, such as the test accuracy, from knowledge of the training data is currently lacking. Huge simplifications arise in the infinite-width limit, in which the number of...
Similarly, traditional backdoors on neural networks also exploit this overparameterization of neural networks; however, while a backdoor is generally used by adversaries for malicious ends (e.g., misclassifying stop signs with stickers as speed limit signs Gu et al., 2017), backdoor watermarks ...
of samples than that suggested by the Nyquist-Shannon limit. This entails the measuring of the signals in already compressed form followed by their decompression at a more convenient stage into a Nyquist-sampled version before proceeding with their processing. Benefits of CS technology include ...
ResNeXt、InceptionResNet、分组卷积的关系? 上图a、b、c分别是ResNeXt、InceptionResNet相似块、分组卷积,通过计算分析,上图三种结构确实等价,b与c等价是显然的 a与 b 等价的原因是:在 a 中,4维特征图通过1\1卷积变为256维,然后32个256维数据求和,而在 b 中,是先将4维数据 concat 成128维,在利用1x1卷积...
2.1 Learning Methods of Spiking Neural Networks ANN到SNN的转换(ANN2SNN)[20, 4, 46, 49, 12, 11, 6, 54, 33]和具有替代梯度的反向传播[40]是获得深度SNN的两种主要方法。ANN2SNN方法首先用ReLU激活训练ANN,然后通过用脉冲神经元替换ReLU并添加缩放操作(如权重归一化和阈值平衡)将ANN转换为SNN。最近的一...