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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
- What is a cascaded convolutional neural network?
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) This combination requires the introduction of a new input feature which fulfills the "cascade manner" and
- convolutional neural networks - When to use Multi-class CNN vs. one . . .
0 I'm building an object detection model with convolutional neural networks (CNN) and I started to wonder when should one use either multi-class CNN or a single-class CNN
- What are the features get from a feature extraction using a CNN?
So, the convolutional layers reduce the input to get only the more relevant features from the image, and then the fully connected layer classify the image using those features, isn't it? I think I've just understood how a CNN works
- In a CNN, does each new filter have different weights for each input . . .
Typically for a CNN architecture, in a single filter as described by your number_of_filters parameter, there is one 2D kernel per input channel There are input_channels * number_of_filters sets of weights, each of which describe a convolution kernel So the diagrams showing one set of weights per input channel for each filter are correct
- deep learning - Artificial Intelligence Stack Exchange
Why do we need convolutional neural networks instead of feed-forward neural networks? What is the significance of a CNN? Even a feed-forward neural network will able to solve the image classificat
- Extract features with CNN and pass as sequence to RNN
But if you have separate CNN to extract features, you can extract features for last 5 frames and then pass these features to RNN And then you do CNN part for 6th frame and you pass the features from 2,3,4,5,6 frames to RNN which is better The task I want to do is autonomous driving using sequences of images
- 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
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