Semi-supervised learning (SSL) has tremendous value in practice due to the utilization of
both labeled and unlabelled data. An essential class of SSL methods, referred to as graph …

State-of-the-art deep learning models are often trained with a large amount of costly labeled
training data. However, requiring exhaustive manual annotations may degrade the model's …

Convolutional neural network (CNN) has demonstrated impressive ability to represent
hyperspectral images and to achieve promising results in hyperspectral image classification …

In many different classification tasks it is required to manage structured data, which are
usually modeled as graphs. Moreover, these graphs can be dynamic, meaning that the …

The recently proposed self-ensembling methods have achieved promising results in deep
semi-supervised learning, which penalize inconsistent predictions of unlabeled data under …

Predicting all applicable labels for a given image is known as multi-label classification.
Compared to the standard multi-class case (where each image has only one label), it is …

Semi-supervised image classification aims to classify a large quantity of unlabeled images
by typically harnessing scarce labeled images. Existing semi-supervised methods often …

Open-set semi-supervised learning (open-set SSL) investigates a challenging but practical
scenario where out-of-distribution (OOD) samples are contained in the unlabeled data …

It is expensive and difficult to precisely annotate objects with multiple labels. Instead, in
many real tasks, annotators may roughly assign each object with a set of candidate labels …

Multi-label classification algorithms based on supervised learning use all the labeled data to
train classifiers. However, in real life, many of the data are unlabeled, and it is costly to label …