How is a convolutional neural network able to learn invariant features? A Taxonomy of Deep Convolutional Neural Nets for Computer Vision Honglak Lee,et al, “Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations” (link)...
卷积神经网络(Convolutional Neural Networks, CNN)是一类包含卷积计算的前馈神经网络,是基于图像任务的平移不变性(图像识别的对象在不同位置有相同的含义)设计的,擅长应用于图像处理等任务。在图像处理中,图像数据具有非常高的维数(高维的RGB矩阵表示),因此训练一个标准的前馈网络来识别图像将需要成千上万的输入神经元...
CNN最经典的案例应该是LeNet-5这个数字识别的任务了吧。这里可以看下Yann Lecun大牛网页 http://yann.lecun.com/exdb/lenet/index.html, 以及tutorial: http://deeplearning.net/tutorial/lenet.html。 另外,一篇比较详细的讲CNN的中文博客(懒得看英语的话,就直接看这篇博客了):http://blog.csdn.net/zouxy...
Recently, deep learning has drawn much attention in computer vision because of its exceptional performance in image classification tasks. For this reason, its application to connectomic analyses holds great promise, as well. In this paper, we introduce a novel deep neural network architecture, ...
machine-learning deep-learning graph graph-convolutional-networks graph-neural-networks Updated Jan 6, 2023 dsgiitr / graph_nets Star 1.2k Code Issues Pull requests PyTorch Implementation and Explanation of Graph Representation Learning papers: DeepWalk, GCN, GraphSAGE, ChebNet & GAT. pytorch deep...
As with any deep neural network, a CNN is trained by passing batches of training data through it over multiple epochs, adjusting the weights and bias values based on the loss calculated for each epoch. In the case of a CNN, backpropagation of adjusted weights includes filter kernel weights...
提出使用“multi-scale”CNN对图像去模糊,采用“端对端”(end-to-end)的方式,即输入一张模糊图像,网络将输出一张同等大小的清晰图像;提出“multi-scale”损失函数,用于模拟传统的“coarse-to-fine”的去模糊方法 提出较大规模的GOPRO数据集,一共由3214对模糊-清晰图像构成Introduction...
We present a deep convolutional recurrent neural network for speech emotion recognition based on the log-Mel filterbank energies, where the convolutional layers are responsible for the discriminative feature learning. Based on the hypothesis that a better understanding of the internal configuration within...
Better understanding of the dose-toxicity relationship is critical for safe dose escalation to improve local control in late-stage cervical cancer radiotherapy. In this study, we introduced a convolutional neural network (CNN) model to analyze rectum dose distribution and predict rectum toxicity. Forty...
However, because of the limited time, future research needs to be done: In transfer learning, training a fine-tuned deep neural network with unfrozen ConvLayers tends to overfit. What can effective methods be done to stabilize the training process? Other more powerful CNN model, such as Res...