A cnn will learn to recognize patterns across space while rnn is useful for solving temporal data problems In a cnn (such as google's inception network), bottleneck layers are added to reduce the number of feature maps (aka channels) in the network, which, otherwise, tend to increase in each layer What is your knowledge of rnns and cnns
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Do you know what an lstm is?
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. A convolutional neural network (cnn) that does not have fully connected layers is called a fully convolutional network (fcn) See this answer for more info
Pooling), upsampling (deconvolution), and copy and crop operations. The concept of cnn itself is that you want to learn features from the spatial domain of the image which is xy dimension So, you cannot change dimensions like you mentioned. What will a host on an ethernet network do if it receives a frame with a unicast destination mac address that does not match its own mac address
It will discard the frame
It will forward the frame to the next host It will remove the frame from the media
