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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 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
- 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
- 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
- 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
- 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
- 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
- Reduce receptive field size of CNN while keeping its capacity?
One way to keep the capacity while reducing the receptive field size is to add 1x1 conv layers instead of 3x3 (I did so within the DenseBlocks, there the first layer is a 3x3 conv and now followed by 4 times a 1x1 conv layer instead of the original 3x3 convs (which increase the receptive field)) In doing that, the number of parameters can be kept at a similar level While 1x1 convolutions are
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