GitHub

Eliminating Catastrophic Overfitting Via Abnormal Adversarial Examples Regularization

Single-step adversarial training (SSAT) has demonstrated the potential to achieve both efficiency and robustness. However, SSAT suffers from catastrophic overfitting (CO), a phenomenon that leads to a ...
IEEE

Overfitting Problem of ANN- and VSTF-Based Nonlinear Equalizers Trained on Repeated Random Bit Sequences

Abstract: In this paper, we investigated the overfitting characteristics of nonlinear equalizers based on an artificial neural network (ANN) and the Volterra series transfer function (VSTF), which ...
IEEE

Addressing the Overfitting in Partial Domain Adaptation With Self-Training and Contrastive Learning

Abstract: Partial domain adaptation (PDA) assumes that target domain class label set is a subset of that of source domain, while this problem setting is close to the actual scenario. At present, there ...
BizTech

Overfitting in Machine Learning: What is it & How Can It Be Prevented?

Ernie Smith is a former contributor to BizTech, an old-school blogger who specializes in side projects, and a tech history nut who researches vintage operating systems for fun. In data analysis, it is ...