In addition to good training data and the right model architecture, loss functions are one of the most important parts of training an accurate machine learning model.
For this post, I’d love to give developers an overview of some of the more advanced loss functions and how they can be used to improve the accuracy of models—or solve entirely new tasks.
For example, semantic segmentation models typically use a simple cross-categorical entropy loss function during training, but if we want to segment objects with many fine details like hair, adding a gradient loss function to the model can vastly improve results.
Continue reading “Research Guide: Advanced Loss Functions for Machine Learning Models”
