… analytically derive the derivative of the dissimilarity metric with respect to the strain using the
chain rule, and therefore, can use an efficient gradient-… NCC as our similarity metric. NCC is, …

… Depending on what underlying structure the analyst wishes to reveal, similarity matrices can
… gradients (both the gradient and the direction of the gradient) within the original data fields. …

… , which we call deep perceptual similarity metrics (DeePSiM), … This metric reflects perceptual
similarity of images much better … behavior and provide strong gradients during training, we …

… ever so often define the lacking gradients in these models in a heuristic … Estimating gradients
in SBNs is the main problem that we … The cosine similarity metric measured in Fig. 3 is more …

… We analytically justify this optimization approach in Section … gradients works better for
optimizing contextual similarity. Unsupervised Metric Learning The concept of contextual similarity …

… metric allows the RD model to be determined analytically, as indicated by (10) and (11). This
nice feature, however, is lost when the traditional distortion metric … iterative gradient descent …

… many similarities that were not metrics—we call them nonmetric similarity functions. In this
article we survey domains employing nonmetric functions for effective similarity search, and …

… We derive and implement analytic energy gradients for the single-reference driven similarity
… equations does not introduce formal difficulties in the gradient theory. We apply the DSRG-…

… or gradient method to minimize the chosen measure of departure from monotonicity. This is
not because gradient … In fact the gradient method has two rather frustrating properties. First, …

Since its introduction in 2004, the structural similarity (SSIM) … ) metrics based on the important
ingredients of SSIM. We then show that such modified measures are valid distance metrics …