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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 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
- What is a cascaded convolutional neural network?
The expression cascaded CNN apparently refers to the fact that equation 1 1 is used iteratively, so there will be multiple CNNs, one for each iteration k k In fact, in the paper, they say Unlike existing CNN methods that apply different network structures for different fitting stages, 3DDFA employs a unified network structure across the cascade
- 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
- What are the features get from a feature extraction using a CNN?
By visualizing the activations of these layers we can take a look on what these high-level features look like The top row here is what you are looking for: the high-level features that a CNN extracts for four different image types
- 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
- When training a CNN, what are the hyperparameters to tune first?
I am training a convolutional neural network for object detection Apart from the learning rate, what are the other hyperparameters that I should tune? And in what order of importance? Besides, I r
- How to use CNN for making predictions on non-image data?
You can use CNN on any data, but it's recommended to use CNN only on data that have spatial features (It might still work on data that doesn't have spatial features, see DuttaA's comment below) For example, in the image, the connection between pixels in some area gives you another feature (e g edge) instead of a feature from one pixel (e g color) So, as long as you can shaping your data
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