A Self-Supervised Method For infrared and Visible Image Fusion

Siamese networks are widely used in various contrastive learning methods for recognition tasks, with few labeled data and abundant unlabeled data. in the field of fault diagnosis, it is universal to face the problem that large collections of common fault data and few catastrophic fault samples result in the imbalanced distribution of fault data collection. in this paper, a simple Siamese framework is proposed to learn meaningful signal representations using the differently augmented views of the signals only in the time domain. The industrial fault diagnosis including class balanced and imbalanced motor fault diagnosis is performed to verify the validity of the signal representations. The results demonstrate that the proposed method can significantly balance the representations of both the major and minor classes, which proves the capability of the Siamese framework for class imbalanced classification.