This article surveys recent work in active learning aimed at making it more practical for real-
world use. In general, active learning systems aim to make machine learning more …

The vision transformer (ViT) is a state-of-the-art architecture for image recognition tasks that
plays an important role in digital health applications. Medical images account for 90% of the …

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 …

The explosive growth in Big Data has attracted much attention in designing efficient indexing
and search methods recently. In many critical applications such as large-scale search and …

Similarity search (nearest neighbor search) is a problem of pursuing the data items whose
distances to a query item are the smallest from a large database. Various methods have …

Entity matching is the problem of identifying which records refer to the same real-world
entity. It has been actively researched for decades, and a variety of different approaches …

We propose a new active learning by query synthesis approach using Generative
Adversarial Networks (GAN). Different from regular active learning, the resulting algorithm …

Active learning and crowdsourcing are promising ways to efficiently build up training sets for
object recognition, but thus far techniques are tested in artificially controlled settings …

This paper proposes ASAL, a new GAN based active learning method that generates high
entropy samples. Instead of directly annotating the synthetic samples, ASAL searches …

Recent years have witnessed the quick progress of the hyperspectral images (HSI)
classification. Most of existing studies either heavily rely on the expensive label information …