Active learning (AL) attempts to maximize a model's performance gain while annotating the
fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount …

Despite the availability and ease of collecting a large amount of free, unlabeled data, the
expensive and time-consuming labeling process is still an obstacle to labeling a sufficient …

The performance of deep neural networks improves with more annotated data. The problem
is that the budget for annotation is limited. One solution to this is active learning, where a …

Data collection is a major bottleneck in machine learning and an active research topic in
multiple communities. There are largely two reasons data collection has recently become a …

Active learning aims to develop label-efficient algorithms by sampling the most
representative queries to be labeled by an oracle. We describe a pool-based semi …

Common acquisition functions for active learning use either uncertainty or diversity
sampling, aiming to select difficult and diverse data points from the pool of unlabeled data …

Convolutional neural networks (CNNs) have been successfully applied to many recognition
and learning tasks using a universal recipe; training a deep model on a very large dataset of …

Deep neural network has been extensively applied to hyperspectral image (HSI)
classification recently. However, its success is greatly attributed to numerous labeled …

In this work, we provide a survey of active learning (AL) for its applications in natural
language processing (NLP). In addition to a fine-grained categorization of query strategies …

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 …