Self-Penalizing Neural Networks: Built-in Regularization Through Internal Confidence Feedback

Abstract

Despite their great efficiency, neural networks may suffer from overfitting that is, when the model performs well on training information but fails to generalize to these fresh inputs. Their inclination to recall patterns rather than acquire representations that go beyond the surface on which they are taught results in this restriction. We provide an original approach called Self-Penalizing Neural Networks (SPNNs) to solve this problem. This idea revolves around an internal confidence feedback mechanism serving the model as its beyond natural conscience. Instead of depending on outside regularizing techniques, SPNNs constantly evaluate their own confidence throughout training and apply penalties when they show too high confidence regarding predictions that later turn out to be faulty. This self-awareness reduces overconfidence & promotes better generalization by thus encouraging an intrinsic drive for moderation & balance. We describe the architectural changes needed to create this internal feedback loop and give a more comprehensive evaluation across standard benchmarks proving that SPNNs outperform conventional regularization methods such as dropout and weight decay in maintaining accuracy on validation & also test sets. This self-regulating behavior improves resilience and fits more closely with practical uses, where overconfidence in inaccurate projections might have dire consequences. In a medical diagnostic setting, where the self-penalizing feature of the model is too crucial to avoid faulty positives, we use SPNNs. Our findings show that incorporating reflective capabilities into learning systems opens a potential path for creating more consistent and trustworthy AI