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- machine learning - What is a fully convolution network? - Artificial . . .
Fully convolution networks A fully convolution network (FCN) is a neural network that only performs convolution (and subsampling or upsampling) operations Equivalently, an FCN is a CNN without fully connected layers Convolution neural networks The typical convolution neural network (CNN) is not fully convolutional because it often contains fully connected layers too (which do not perform the
- machine learning - What is the concept of channels in CNNs . . .
The concept of CNN itself is that you want to learn features from the spatial domain of the image which is XY dimension So, you cannot change dimensions like you mentioned
- deep learning - What are bottlenecks in neural networks? - Artificial . . .
In a CNN (such as Google's Inception network), bottleneck layers are added to reduce the number of feature maps (aka channels) in the network, which, otherwise, tend to increase in each layer This is achieved by using 1x1 convolutions with fewer output channels than input channels
- neural networks - Are fully connected layers necessary in a CNN . . .
A convolutional neural network (CNN) that does not have fully connected layers is called a fully convolutional network (FCN) See this answer for more info An example of an FCN is the u-net, which does not use any fully connected layers, but only convolution, downsampling (i e pooling), upsampling (deconvolution), and copy and crop operations
- What is a cascaded convolutional neural network?
3 The paper you are citing is the paper that introduced the cascaded convolution neural network In fact, in this paper, the authors say To realize 3DDFA, we propose to combine two achievements in recent years, namely, Cascaded Regression and the Convolutional Neural Network (CNN)
- How to handle rectangular images in convolutional neural networks . . .
I think the squared image is more a choice for simplicity There are two types of convolutional neural networks Traditional CNNs: CNNs that have fully connected layers at the end, and fully convolutional networks (FCNs): they are only made of convolutional layers (and subsampling and upsampling layers), so they do not contain fully connected layers With traditional CNNs, the inputs always need
- What is the fundamental difference between CNN and RNN?
A CNN will learn to recognize patterns across space while RNN is useful for solving temporal data problems CNNs have become the go-to method for solving any image data challenge while RNN is used for ideal for text and speech analysis
- neural networks - How do I choose the optimal batch size? - Artificial . . .
Here are a few guidelines, inspired by the deep learning specialization course, to choose the size of the mini-batch: If you have a small training set, use batch gradient descent (m < 200) In practice: Batch mode: long iteration times Mini-batch mode: faster learning Stochastic mode: lose speed up from vectorization The typically mini-batch sizes are 64, 128, 256 or 512 And, in the end, make
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