C4W4L03 Siamese Network

Key Takeaways

the job of the function D which you learned about in the last video is to input two faces and tell you how similar or how different they are a good way to do this is to use a Siamese Network let's take a look you're used to seeing pictures of confidence like these where you input an image let's say X 1 and through a sequence of convolutional and pulling and fully connected layers and with a feature vector like that and sometimes this is fed to a softmax unit to make a classification but we're gonna not going to use that in this video instead we're going to focus on this vector of let's say 128 numbers computed by some fully connected layer that is deeper in the network and I'm going to give this this of 128 numbers a name I'm going to call this f of X 1 and you should think of f of X 1 as an encoding of the input image X 1 so it's taken the input image here this picture of kion and is re representing it as a vector of 128 numbers the way you can build a face-recognition system is then that if you want to compare two pictures let's say this first picture with this second picture here what you can do is feed the second picture to the same neural network with the same parameters and get a different vector of 128 numbers which represents or which encodes the second picture so I'm going to call this second picture so I'm going to call this encoding of this second picture f of X 2 and here I'm using X 1 and X 2 just to denote two input images they don't necessarily have to be the first and second examples in your training sets it can be any two pictures finally if you believe that these encoding x' are a good representation of these two images what you can do is then to find the image of distance between X 1 and X 2 has the norm of the difference between the encoding of these two images so this idea of running two identical convolutional neural networks on two different inputs and then comparing them sometimes that's called a Siamese neural network architecture and a lot of the ideas I'm presenting here came from this paper due to yawn of Tackman Minyoung marker really Renato and Leo wolf in a research system that they developed called deep face and many of the ideas I'm presenting here came from a paper due to yawn of Todman Minyoung marker really Renato and Leo wolf in the system that they developed called deep face so how do you train this siamese neural network remember that these two neural networks have the same parameters so what you want to do is really train a neural network so that the encoding that it computes results in a function D that tells you when two pictures are of the same person so more formally the parameters of the neural network define an encoding f of X I so given any input image X I the neural network oprah's this engine 28 dimensional encoding f of X I so more formally what you want to do is learn parameters so that if two pictures X I and XJ are of the same person then you want that distance between their encodings to be small and in the previous slide I was using x1 and x2 but is really any pair X I and XJ from your training set and in contrast if X I XJ are of different persons then you want that distance between their encodings to be large so as you vary the parameters and all of these layers of the neural net where you end up with different and coatings and what you can do is use back propagation to vary all those parameters in order to make sure these conditions are satisfied so you've learned about the siamese network architecture and have a sense of what you want the neural network to output for you in terms of what would make a good encoding but how do you actually define an objective function to make your new network learn to do what we just discussed here let's see how you can do that to the next video using the triplet loss function