👉 Pre-loss is a technique used in deep learning models to mitigate the effects of overfitting. It involves subtracting out the loss during training, which helps prevent the model from becoming too complex and biased towards certain classes. This approach can help improve generalization performance by reducing the amount of data that needs to be fed into the model at each epoch.
