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
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
When training a CNN, what are the hyperparameters to tune first? Firstly when you say an object detection CNN, there are a huge number of model architectures available Considering that you have narrowed down on your model architecture a CNN will have a few common layers like the ones below with hyperparameters you can tweak: Convolution Layer:- number of kernels, kernel size, stride length, padding
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
What is the computational complexity of the forward pass of a . . . Stack Exchange Network Stack Exchange network consists of 183 Q A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers
What to do if CNN cannot overfit a training set on adding dropout? I have been trying to use CNN for a regression problem I followed the standard recommendation of disabling dropout and overfitting a small training set prior to trying for generalization With a 10 layer deep architecture, I could overfit a training set of about 3000 examples
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