Fusion of standard and ordinal dropout techniques to regularise deep models

Research areas:

• Uncategorized

Year:

2024

Type of Publication:

Article

Keywords:

Deep Learning, Dropout, Ordinal Classification, Ordinal Regression, Convolutional Neural Networks

Authors:

• Bérchez-Moreno, Francisco
• Fernández, Juan Carlos
• Hervás-Martínez, César
• Gutiérrez, Pedro Antonio

Journal:

Information Fusion

Volume:

106

Month:

Febrero

BibTex:

@article{ordinal_dropout_2024, author = "Francisco B{\'e}rchez-Moreno and Juan Carlos Fern{\'a}ndez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Pedro Antonio Guti{\'e}rrez", abstract = "Dropout is a popular regularisation tool for deep neural classifiers, but it is applied regardless of the nature of the classification task: nominal or ordinal. Consequently, the order relation between the class labels of ordinal problems is ignored. In this paper, we propose the fusion of standard dropout and a new dropout methodology for ordinal classification regularising deep neural networks to avoid overfitting and improve generalisation, but taking into account the extra information of the ordinal task, which is exploited to improve performance. The correlation between the outputs of every neuron and the target labels is used to guide the dropout process: the higher the neuron is correlated with the expected labels, the lower its probability of being dropped. Given that randomness also plays a crucial role in the regularisation process, a balancing factor () is also added to the training process to determine the influence of the ordinality with respect to a constant probability, providing a hybrid ordinal regularisation method. An extensive battery of experiments shows that the new hybrid ordinal dropout methodology perform better than standard dropout, obtaining improved results in most evaluation metrics, including not only ordinal metrics but also nominal ones.", awards = "JCR 2022: 18.6, Position: 2/111 (Q1), Category: COMPUTER SCIENCE, THEORY {\&}amp; METHODS", comments = "JCR 2022: 18.6, Position: 2/111 (Q1), Category: COMPUTER SCIENCE, THEORY {\&}amp; METHODS", doi = "10.1016/j.inffus.2024.102299", journal = "Information Fusion", keywords = "Deep Learning, Dropout, Ordinal Classification, Ordinal Regression, Convolutional Neural Networks", month = "Febrero", note = "JCR 2022: 18.6, Position: 2/111 (Q1), Category: COMPUTER SCIENCE, THEORY {\&}amp; METHODS", title = "{F}usion of standard and ordinal dropout techniques to regularise deep models", url = "www.sciencedirect.com/science/article/pii/S1566253524000770", volume = "106", year = "2024", }

Note:

JCR 2022: 18.6, Position: 2/111 (Q1), Category: COMPUTER SCIENCE, THEORY & METHODS

Abstract:

Dropout is a popular regularisation tool for deep neural classifiers, but it is applied regardless of the nature of the classification task: nominal or ordinal. Consequently, the order relation between the class labels of ordinal problems is ignored. In this paper, we propose the fusion of standard dropout and a new dropout methodology for ordinal classification regularising deep neural networks to avoid overfitting and improve generalisation, but taking into account the extra information of the ordinal task, which is exploited to improve performance. The correlation between the outputs of every neuron and the target labels is used to guide the dropout process: the higher the neuron is correlated with the expected labels, the lower its probability of being dropped. Given that randomness also plays a crucial role in the regularisation process, a balancing factor () is also added to the training process to determine the influence of the ordinality with respect to a constant probability, providing a hybrid ordinal regularisation method. An extensive battery of experiments shows that the new hybrid ordinal dropout methodology perform better than standard dropout, obtaining improved results in most evaluation metrics, including not only ordinal metrics but also nominal ones.

Comments:

JCR 2022: 18.6, Position: 2/111 (Q1), Category: COMPUTER SCIENCE, THEORY & METHODS