What is soli loss?

Soli loss, often referred to in the context of machine learning and neural networks, is a type of loss function used to measure the discrepancy between predicted outputs and actual labels. It is typically employed in tasks like classification or regression to optimize model performance during training. By minimizing soli loss, the model improves its accuracy and generalization capabilities. The term "soli" itself may not be widely recognized; it might be a typographical error or confusion with "silhouette loss" or another specific loss function.