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Can CNN and RNN be used together?
Taking advantage of the strengths of both CNN and RNN, the combination outperforms those individual models. Another method to combine them together is to let RNN encode the input representation and feed the outputs into CNN [16][17].
Can you combine neural networks?
Yes you can. There are three ways I can think of, depending on your requirement. Have the two neural networks independent and train them separately, but combine the output just like ensemble model. Make a brand new neural network using logics and algorithms of the two neural networks.
Is LSTM a CNN?
The CNN Long Short-Term Memory Network or CNN LSTM for short is an LSTM architecture specifically designed for sequence prediction problems with spatial inputs, like images or videos.
How is a combination of CNN and RNN done?
Combinations of CNN and RNN The general architecture of these combinations is a convolutional feature extractor applied on the input, then some recurrent network on top of the CNN’s output, then an optional fully connected layer on RNN’s output and finally a softmax layer.
How are RNN and CNN used for object detection?
In a mixed CNN and RNN architecture the positive features of a RNN are used to improve the CNN. Liang and Hu are describing an architecture for object detection in (6) and in (2) a similar architecture for scene labeling is proposed. In these papers the combined network is called RCNN.
When to use LSTMs with CNN and RNN?
Researchers have used layered CNN-RNN pairs where the output of the CNN is input to the RNN. Logically the RNN has also been replaced with LSTMs to create a more ‘in the moment’ description of each video segment.
When to use MLP, CNN, and RNN neural networks?
Convolutional Neural Networks, or CNNs, were designed to map image data to an output variable. They have proven so effective that they are the go-to method for any type of prediction problem involving image data as an input.