Machine learning has become successful in solving wireless interference management problems. Different kinds of deep neural networks (DNNs) have been trained to accomplish key tasks such as power cont

Self-supervised contrastive learning is a powerful tool to learn visual representation without labels. Prior work has primarily focused on evaluating the recognition accuracy of various pre-training a

Deep learning on graphs has recently achieved remarkable success on a variety of tasks while such success relies heavily on the massive and carefully labeled data. However, precise annotations are gen

Noisy labels, resulting from mistakes in manual labeling or webly data collecting for supervised learning, can cause neural networks to overfit the misleading information and degrade the generalizatio

This paper tackles the problem of semi-supervised learning when the set of labeled samples is limited to a small number of images per class, typically less than 10, problem that we refer to as barely-

Self-supervised Learning (SSL) including the mainstream contrastive learning has achieved great success in learning visual representations without data annotations. However, most methods mainly focus

Scene flow is the three-dimensional (3D) motion field of a scene. It provides information about the spatial arrangement and rate of change of objects in dynamic environments. Current learning-based ap

Supervised and semi-supervised learning methods have been traditionally designed for the closed-world setting based on the assumption that unlabeled test data contains only classes previously encounte

The renaissance of artificial neural networks was catalysed by the success of classification models, tagged by the community with the broader term supervised learning. The extraordinary results gave r

We study unsupervised data selection for semi-supervised learning (SSL), where a large-scale unlabeled data is available and a small subset of data is budgeted for label acquisition. Existing SSL meth

Recently, unsupervised adversarial training (AT) has been extensively studied to attain robustness with the models trained upon unlabeled data. To this end, previous studies have applied existing supe

Recent advances in contrastive learning have enlightened diverse applications across various semi-supervised fields. Jointly training supervised learning and unsupervised learning with a shared featur

While deep-learning based methods for visual tracking have achieved substantial progress, these schemes entail large-scale and high-quality annotated data for sufficient training. To eliminate expensi