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Found 6 papers, 3 papers with code
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.
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.
Probabilistic Active Learning for Active Class Selection
In machine learning, active class selection (ACS) algorithms aim to actively select a class and ask the oracle to provide an instance for that class to optimize a classifier's performance while minimizing the number of requests.
Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary
Our approach is to frame SVDD sampling as an optimization problem, where constraints guarantee that sampling indeed approximates the original decision boundary.
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.
Limitations of Assessing Active Learning Performance at Runtime
Hence, it is not possible to reliably estimate the performance of the classification system during learning and it is difficult to decide when the system fulfills the quality requirements (stopping criteria).
