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, ...

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 evaluation ...

However, existing loss functions used for image classification ignore the inherent manifold structure between samples, resulting in limited network performance. In this paper, we reveal that ...

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 ...

Loss functions can be customized using distances, reducers, and regularizers. In the diagram below, a miner finds the indices of hard pairs within a batch. These are used to index into the distance ...

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 ...