Robust Loss Function

Robust loss functions aim to improve the training of machine learning models, particularly deep neural networks, by mitigating the negative effects of noisy or incomplete data labels. Current research focuses on developing novel loss functions, often incorporating adaptive weighting schemes or leveraging self-supervised learning and adversarial training techniques to enhance robustness and generalization. These advancements are crucial for building reliable models in various applications, including object grasping, medical image analysis, and autonomous systems, where data imperfections are common. The ultimate goal is to create more accurate and dependable models even with imperfect training data.