Data augmentation (DA) is indispensable in modern machine learning and deep neural
networks. The basic idea of DA is to construct new training data to improve the model's …

Deep learning methods have attracted much attention in the field of polarimetric synthetic
aperture radar (PolSAR) image classification over the past few years. However, for …

Deep learning methods have been widely studied in the field of polarimetric synthetic
aperture radar (PolSAR) ship detection over the past few years. However, the backscattering …

As an emerging early diagnostic technology for gastrointestinal diseases, confocal laser
endomicroscopy lacks large-scale perfect annotated data, leading to a major challenge in …

Learning better representations is essential in medical image analysis for computer-aided
diagnosis. However, learning discriminative semantic features is a major challenge due to …

Contrastive instance discrimination outperforms supervised learning in downstream tasks
like image classification and object detection. However, this approach heavily relies on data …

As Deep Neural Networks have achieved thrilling breakthroughs in the past decade, data
augmentations have garnered increasing attention as regularization techniques when …

In this paper, we propose a new self-supervised learning (SSL) method for representations
that enable logic operations. Representation learning has been applied to various tasks like …