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 …

Active learning is a label-efficient machine learning method that actively selects the most
valuable unlabeled samples to annotate. Active learning focuses on achieving the best …

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 …

Human-centricity is the core value behind the evolution of manufacturing towards Industry
5.0. Nevertheless, there is a lack of architecture that considers safety, trustworthiness, and …

Requirement of large annotated datasets restrict the use of deep convolutional neural
networks (CNNs) for many practical applications. The problem can be mitigated by using …

Current deep learning methods are regarded as favorable if they empirically perform well on
dedicated test sets. This mentality is seamlessly reflected in the resurfacing area of continual …

In this paper, we are proposing a unified and principled method for both the querying and
training processes in deep batch active learning. We are providing theoretical insights from …

While deep learning succeeds in a wide range of tasks, it highly depends on the massive
collection of annotated data which is expensive and time-consuming. To lower the cost of …

Active learning is to design label-efficient algorithms by sampling the most representative
samples to be labeled by an oracle. In this paper, we propose a state relabeling adversarial …

We propose an active learning approach for transferring representations across domains.
Our approach, active adversarial domain adaptation (AADA), explores a duality between two …