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Found 8 papers, 5 papers with code
Fast Fishing: Approximating BAIT for Efficient and Scalable Deep Active Image Classification
BAIT, a recently proposed AL strategy based on the Fisher Information, has demonstrated impressive performance across various datasets.
Active Label Refinement for Semantic Segmentation of Satellite Images
Since these initial labels are partially erroneous, we use active learning strategies to cost-efficiently refine the labels in the second step.
Multi-annotator Deep Learning: A Probabilistic Framework for Classification
Training standard deep neural networks leads to subpar performances in such multi-annotator supervised learning settings.
Fast Bayesian Updates for Deep Learning with a Use Case in Active Learning
Retraining deep neural networks when new data arrives is typically computationally expensive.
A Review of Uncertainty Calibration in Pretrained Object Detectors
In the field of deep learning based computer vision, the development of deep object detection has led to unique paradigms (e. g., two-stage or set-based) and architectures (e. g., Faster-RCNN or DETR) which enable outstanding performance on challenging benchmark datasets.
A Survey on Cost Types, Interaction Schemes, and Annotator Performance Models in Selection Algorithms for Active Learning in Classification
Therefore, we introduce a general real-world AL strategy as part of a learning cycle and use its elements, e. g., the query and annotator selection algorithm, to categorize about 60 real-world AL strategies.
Toward Optimal Probabilistic Active Learning Using a Bayesian Approach
Gathering labeled data to train well-performing machine learning models is one of the critical challenges in many applications.
Collaborative Interactive Learning -- A clarification of terms and a differentiation from other research fields
The field of collaborative interactive learning (CIL) aims at developing and investigating the technological foundations for a new generation of smart systems that support humans in their everyday life.
