This article recounts the author's struggle with overfitting in their first Convolutional Neural Network for image classification, where high training accuracy contrasted with poor validation results. It aims to explain how to identify and resolve overfitting, offering practical advice to avoid common frustrations.
This research explores a differential-privacy based approach to improve generalization and prevent overfitting in Deep Neural Networks. Overfitting, where models learn noise and perform poorly on unseen data, is a growing challenge in modern AI systems.
This research investigates the "double descent" phenomenon in overparametrized models, which allows for improved generalization despite classical overfitting concerns. The study specifically explores this effect in linear regression with contaminated training data, finding that significant overparametrization enables double descent even in such robust settings.