Next Generation Loss Function for Image Classification

arXiv:2404.12948v1 Announce Type: cross
Abstract: Neural networks are trained by minimizing a loss function that defines the discrepancy between the predicted model output and the target value. The selection of the loss function is crucial to achieve task-specific behaviour and highly influences the capability of the model. A variety of loss functions have been proposed for a wide range of tasks affecting training and model performance. For classification tasks, the cross entropy is the de-facto standard and usually the first …

abstract arxiv capability classification cs.cv cs.lg cs.ne function functions image loss networks neural networks next type value