What is the difference between a convolutional neural network and a . . . A CNN, in specific, has one or more layers of convolution units A convolution unit receives its input from multiple units from the previous layer which together create a proximity Therefore, the input units (that form a small neighborhood) share their weights The convolution units (as well as pooling units) are especially beneficial as:
machine learning - What is a fully convolution network? - Artificial . . . A fully convolutional network is achieved by replacing the parameter-rich fully connected layers in standard CNN architectures by convolutional layers with $1 \times 1$ kernels I have two questions What is meant by parameter-rich? Is it called parameter rich because the fully connected layers pass on parameters without any kind of "spatial
What are the features get from a feature extraction using a CNN? By accessing these high-level features, you essentially have a more compact and meaningful representation of what the image represents (based always on the classes that the CNN has been trained on) By visualizing the activations of these layers we can take a look on what these high-level features look like
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
Extract features with CNN and pass as sequence to RNN $\begingroup$ 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
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