Learn about the advantages and disadvantages of cross-entropy, IoU, focal loss, and GIoU for object detection in deep learning.

Now that we know what a loss function is, let’s see which loss function to use when. Regression loss functions: There are plenty of regression algorithms like linear regression, logistic regression, ...

Abstract: Metric learning approaches have widely expanded to the training of Speaker Verification (SV) systems based on Deep Neural Networks (DNNs), by using a loss function more consistent with the ...

This letter proposes a perceptual metric for speech quality evaluation, which is suitable, as a loss function, for training deep learning methods. This metric, derived from the perceptual evaluation ...

Custom Loss functions for asset return prediction with deep learning regression. Custom loss functions presented improve ML regression algorithms predicting asset returns. This repository is linked to ...

The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch. - KevinMusgrave ... a batch. These are used to index into the distance matrix, ...

Calculate (an approximation of) the predictive density, and 2. Minimize the expectation of the loss function under that (approximated) predictive density. (Empirical risk minimization, on the other ...