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Found 6 papers, 2 papers with code
FOCAL: A Cost-Aware Video Dataset for Active Learning
In this paper, we introduce the FOCAL (Ford-OLIVES Collaboration on Active Learning) dataset which enables the study of the impact of annotation-cost within a video active learning setting.
Example Forgetting: A Novel Approach to Explain and Interpret Deep Neural Networks in Seismic Interpretation
In recent years, deep neural networks have significantly impacted the seismic interpretation process.
Gaussian Switch Sampling: A Second Order Approach to Active Learning
To alleviate this issue, we propose a grounded second-order definition of information content and sample importance within the context of active learning.
Forgetful Active Learning with Switch Events: Efficient Sampling for Out-of-Distribution Data
However, existing strategies directly base the data selection on the data representation of the unlabeled data which is random for OOD samples by definition.
Explaining Deep Models through Forgettable Learning Dynamics
Inspired by this phenomenon, we present a novel segmentation method that actively uses this information to alter the data representation within the model by increasing the variety of difficult regions.
Patient Aware Active Learning for Fine-Grained OCT Classification
For this purpose, we propose a framework that incorporates clinical insights into the sample selection process of active learning that can be incorporated with existing algorithms.
