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I found Keras API that allows to add additional time dimension.

model = Sequential()
# define CNN model
model.add(TimeDistributed(Conv2D(...))
model.add(TimeDistributed(MaxPooling2D(...)))
model.add(TimeDistributed(Flatten()))
# define LSTM model
model.add(LSTM(...))
model.add(Dense(...))

I thought that this is only something that allows to write shorter code because we don't need to use loop over images and extracting features. But I read somewhere that it is not the same as extracting features with a CNN, ad then passing the sequence to a separate RNN.

So what is the difference between these two solutions? What is better in what situations?

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