等变表示Equivariant Representation:如果一个函数满足输入改变,输出也以同样的方式改变,那么就可以称这个函数是等变的,如果对于函数f和g有\(f(g(x))=g(f(x))\)那么就称f和g具有等变性。 对于卷积而言,参数共享的特殊形式是的神经网络层具有对平移操作等变的性质,这里可以令g为平移函数,那么g就可以表示图像函...
1、什么是CNN 卷积神经网络(Convolutional Neural Networks, CNN)是一类包含卷积计算且具有深度结构的前馈神经网络(Feedforward Neural Networks),是深度学习(deep learning)的代表算法之一。卷积神经网络具有表征学习(representation learning)能力,能够按其阶层结构对输入信息进行平移不变分类(shift-invariant classification),...
Convolutional neural networks (CNNs) are one of the most successful deep learning architectures designed to learn representation from an input signal with different levels of abstraction23. A typical CNN includes convolutional layers which learn spatially invariant features from input (i.e. invariance ...
Fig. 6. Representation of convolutional neural network architecture. Convolution layer aims to slide a predefined fixed-size window on the feature map. Each feature tiles at various positions are extracted adjacent step by step, and obtained vector space is reorganized to acquire a new tensor. The...
(image and filter, in this case) are similar. This is called a filter matrix, which is also known as a filter kernel or just a filter. The results are then passed into the pooling layer, which generates a feature map—a representation of the input data that identifies important features....
1)Word Representation Learning 我们结合单词和其上下文对单词进行表征,上下文有助于获得更精确的单词表征,在模型中我们使用双向recurrent neural network去捕获上下文。定义c_l(w_i)为单词wi的左侧文本,c_r(w_i)为单词wi的右侧文本, 两侧都是有着c个值的紧密向量,左侧的向量c_l(w_i)通过式1计算,其中e(w_...
The role of the fully connected layer is to map the learned “distributed feature representation” to the sample labeling space, which is simply equivalent to a “classifier”. View article Chapter Medical Image Analysis With Deep Neural Networks Deep Learning and Parallel Computing Environment for ...
Convolutional Neural Network (CNN)可以说是近几年最火的算法之一了,凡做图像必用CNN,因为其良好的local representation的能力可以有效提取到图像的局部特征。最近CNN也被广泛应用到NLP领域,本证明学习能力依然出众。基本的CNN模型可以参见Stanford CS231n课程CNN for visual recogonition。本文使用的基本CNN结构包括一层...
Deep learning with Convolutional Neural Networks has shown great promise in image-based classification and enhancement but is often unsuitable for predictive modeling using features without spatial correlations. We present a feature representation approach termed REFINED (REpresentation of Features as Images ...
特征提取及特征工程不仅需要专业的领域知识,而且需要花费大量人力物力。深度学习模型是一种表示学习 (representation learning),能够学到数据更高层次的抽象表示,能够自动从数据中提取特征。 另外,深度学习的模型能力会随着深度的增加而呈指数增长。 Yann Lecun等人在 1989年提出基于梯度学习的卷积神经网络算法[1],并...