The world we see is ever-changing and it always changes with people, things, and the
environment. Domain is referred to as the state of the world at a certain moment. A research …

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 …

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 …

Despite the substantial progress of active learning for image recognition, there still lacks an
instance-level active learning method specified for object detection. In this paper, we …

Recent successes in learning-based image classification, however, heavily rely on the large
number of annotated training samples, which may require considerable human effort. In this …

Investigating active learning, we focus on the relation between the number of labeled
examples (budget size), and suitable querying strategies. Our theoretical analysis shows a …

The existing active learning methods select the samples by evaluating the sample's
uncertainty or its effect on the diversity of labeled datasets based on different task-specific or …

Active learning (AL) combines data labeling and model training to minimize the labeling cost
by prioritizing the selection of high value data that can best improve model performance. In …

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 …

Active learning is a promising alternative to alleviate the issue of high annotation cost in the
computer vision tasks by consciously selecting more informative samples to label. Active …