Machine learning has been heavily researched and widely used in many disciplines.
However, achieving high accuracy requires a large amount of data that is sometimes …

Which samples should be labelled in a large dataset is one of the most important problems
for the training of deep learning. So far, a variety of active sample selection strategies related …

The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent
years. Contrary to conventional approaches to AI where tasks are solved from scratch using …

Generally, data is available abundantly in unlabeled form, and its annotation requires some
cost. The labeling, as well as learning cost, can be minimized by learning with the minimum …

Meta-learning, or learning to learn, is the science of systematically observing how different
machine learning approaches perform on a wide range of learning tasks, and then learning …

Learning-based approaches for semantic segmentation have two inherent challenges. First,
acquiring pixel-wise labels is expensive and time-consuming. Second, realistic …

Collective human knowledge has clearly benefited from the fact that innovations by
individuals are taught to others through communication. Similar to human social groups …

Model-agnostic meta-learning (MAML) is arguably one of the most popular meta-learning
algorithms nowadays. Nevertheless, its performance on few-shot classification is far behind …

Training data creation is increasingly a key bottleneck for developing machine learning,
especially for deep learning systems. Active learning provides a cost-effective means for …

Active learning (AL) is an effective sample selection approach that annotates only a subset
of the training data to address the challenge of data annotation, and deep learning (DL) is …