GradNone

GradNone is a training technique used in machine learning and deep learning to improve model robustness and generalization. It involves intentionally introducing certain types of randomness or noise into the training process, which helps the model learn to handle variations and uncertainties in real-world data. This approach prevents the model from relying too heavily on specific patterns or features, reducing overfitting and enhancing its ability to perform well on new, unseen data. Essentially, GradNone encourages the model to develop a more flexible and resilient understanding, leading to more reliable and accurate predictions.