Ensemble Co-Teaching for Robust Learning of Deep Neural Networks under Label Noise - Miyaji, Renato O.; Corrêa, Pedro L. P. (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2024: Anais do XXI Encontro Nacional de Inteligência Artificial e Computacional, 2024)
Training Deep Neural Networks under Label Noise is challenging due to their memorization ability. To address this issue, various methods have been developed to facilitate robust learning under such conditions. Methods based on multiple networks, such as Stochastic Co-Teaching, have demonstrated superior performance in identifying correctly labeled instances compared to state-of-the-art approaches. In this paper we propose a new method, Ensemble Co-Teaching, which introduces the concept of ensemble learning into robust learning techniques by incorporating perturbations in the network weights. This ensures diversity between the two networks and enhances their ability to detect clean label samples. The proposed Ensemble Co-Teaching method achieved an accuracy improvement, with 91.0% compared to 88.9% from the Co-Teaching method.