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Weakly Supervised Object Detection Based on Active Learning
Weakly supervised object detection which reduces the need for strong supersivison during training has recently made significant achievements....
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Sequential semi-supervised active learning model in extremely low training set (SSSAL)
With the rapid development of computing and multimedia technology, the volume of web traffic data, social networks, sensors and other types of...
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Active Learning for Imbalanced Civil Infrastructure Data
Aging civil infrastructures are closely monitored by engineers for damage and critical defects. As the manual inspection of such large structures is...
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Multi-level membership inference attacks in federated Learning based on active GAN
In recent years, federated learning has been widely used in various fields, such as smart healthcare and financial forecast, due to its ability to...
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Uncertainty Driven Active Learning for Image Segmentation in Underwater Inspection
Active learning aims to select the minimum amount of data to train a model that performs similarly to a model trained with the entire dataset. We...
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Hitting the target: stop** active learning at the cost-based optimum
Active learning allows machine learning models to be trained using fewer labels while retaining similar performance to traditional supervised...
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A generative adversarial active learning method for mechanical layout generation
Layout generation is frequently encountered in the field of mechanical design. The direct application of generative adversarial network, which was...
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Active Transfer Learning for 3D Hippocampus Segmentation
Insufficient data is always a big challenge for medical imaging that is limited by the expensive labeling cost, time-consuming and intensive labor....
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Learning Objective-Specific Active Learning Strategies with Attentive Neural Processes
Pool-based active learning (AL) is a promising technology for increasing data-efficiency of machine learning models. However, surveys show that...
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Active reinforcement learning based approach for localization of target ROI (region of interest) in cervical cell images
The localization of tumour is an important factor towards the detection of malignant cervical cells. A Deep Q-Network (DQN) algorithm was implemented...
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Deep entity matching with adversarial active learning
Entity matching (EM), as a fundamental task in data cleansing and integration, aims to identify the data records in databases that refer to the same...
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Active Learning Strategies on a Real-World Thyroid Ultrasound Dataset
Machine learning applications in ultrasound imaging are limited by access to ground-truth expert annotations, especially in specialized applications...
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Active pairwise distance learning for efficient labeling of large datasets by human experts
In many machine learning applications, the labeling of datasets is done by human experts, which is usually time-consuming in cases of large data...
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Evaluating Zero-Cost Active Learning for Object Detection
Object detection requires substantial labeling effort for learning robust models. Active learning can reduce this effort by intelligently selecting...
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AALpy: an active automata learning library
AALpy is an extensible open-source Python library providing efficient implementations of active automata learning algorithms for deterministic,...
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A Stop** Criterion for Transductive Active Learning
In transductive active learning, the goal is to determine the correct labels for an unlabeled, known dataset. Therefore, we can either ask an oracle...
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Informative Classification of Capsule Endoscopy Videos Using Active Learning
The wireless capsule endoscopy is a non-invasive imaging method that allows observation of the inner lumen of the small intestine, but with the cost...
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An active learning Kriging model with adaptive parameters for reliability analysis
The prevalence of highly nonlinear and implicit performance functions in structural reliability analysis has increased the computational effort...
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Towards Data- and Compute-Efficient Fake-News Detection: An Approach Combining Active Learning and Pre-Trained Language Models
In today’s digital era, dominated by social media platforms such as Twitter , Facebook , and Instagram , the swift dissemination of misinformation...
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A Structural-Clustering Based Active Learning for Graph Neural Networks
In active learning for graph-structured data, Graph Neural Networks (GNNs) have shown effectiveness. However, a common challenge in these...