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...
The kernel slides over the image in small steps, called strides, and performs element-wise multiplications with the corresponding elements of the image and then sums up the results. The output of this operation is called a feature map. When the input is RGB(or more than 3 channels) the ...
Detector: put each activation through element-wise polynomial (coefficients are randomized and kept secret). The model is trained to minimize this polynomial to be under some thresholdt. Assumption is clean samples won't trigger this threshold, but adversarial examples may. Choice of coefficient affe...
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...