What is “linear projection” in convolutional neural network
I am reading through Residual learning , and I have a question. What is "linear projection" mentioned in 3.2? Looks pretty simple once got this but could not get the idea... I am basically not a computer science person, so I would very appreciate if someone provide me a simple example. First up, it's important to understand what x , y and F are and why they need any projection at all. I'll try explain in simple terms, but basic understanding of ConvNets is required. x is an input data (called tensor ) of the layer, in case of ConvNets it's rank is 4. You can think of it as a 4-dimensional