Unable to figure out the linear embedding layer in the convolutional neural network?
Linear embedding layer must be just a fancy name for a dense layer with no activation.
'Linear' means there is no activation (activation is identity). And the embedding is rather a concept for a vector representation of the input data (e.g. word embeddings). I believe the elements from the second vector are simply added to the first one element-wise.