will be asigmoid functionapplied to the difference between the two sets of encodings. The formula below computes the element-wise difference in absolute values between the two encodings: In summary, we just need to create a training set of pairs of images wheretarget label = 1ofsameperson and...
Batch gradient descent computes the gradient using the whole dataset. This is great for convex, or relatively smooth error manifolds. In this case, we move somewhat directly towards an optimum solution, either local or global. Additionally, batch gradient descent, given an annealed learning rate, ...
Minibatch SDG takes a subset of your data and computes the gradient based on that. Relative to SGD, it takes a more direct path to the optimum while keeping us from having to calculate our minimum using the whole dataset. In terms of increasing time (not necessarily number of iterations),...
will be asigmoid functionapplied to the difference between the two sets of encodings. The formula below computes the element-wise difference in absolute values between the two encodings: In summary, we just need to create a training set of pairs of images wheretarget label = 1ofsameperson and...