Weight Rotation as a Regularization Strategy in Convolutional Neural Networks

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2019

Autores

Eduardo Castro (Autor) Outra; José Costa Pereira (Autor) Outra; Jaime S. Cardoso (Autor) FEUP

Ata de Conferência Internacional

Título: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

Páginas: 2106-2110

41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019

23 July 2019 through 27 July 2019

Indexação

ISI Web of Knowledge - 2 Citações

Scopus - 2 Citações

Outras Informações

ID Authenticus: P-00R-JD7

DOI: 10.1109/embc.2019.8856448

Abstract (EN): Convolutional Neural Networks (CNN) have become the gold standard in many visual recognition tasks including medical applications. Due to their high variance, however, these models are prone to over-fit the data they are trained on. To mitigate this problem, one of the most common strategies, is to perform data augmentation. Rotation, scaling and translation are common operations. In this work we propose an alternative method to rotation-based data augmentation where the rotation transformation is performed inside the CNN architecture. In each training batch the weights of all convolutional layers are rotated by the same random angle. We validate our proposed method empirically showing its usefulness under different scenarios.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 5