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Found 4 papers, 1 papers with code
Class-Balanced Active Learning for Image Classification
Results on three datasets showed that the method is general (it can be combined with most existing active learning algorithms) and can be effectively applied to boost the performance of both informative and representative-based active learning methods.
Reducing Label Effort: Self-Supervised meets Active Learning
Our experiments reveal that self-training is remarkably more efficient than active learning at reducing the labeling effort, that for a low labeling budget, active learning offers no benefit to self-training, and finally that the combination of active learning and self-training is fruitful when the labeling budget is high.
When Deep Learners Change Their Mind: Learning Dynamics for Active Learning
Many methods approach this problem by measuring the informativeness of samples and do this based on the certainty of the network predictions for samples.
Temporal Coherence for Active Learning in Videos
Our active learning criterion is based on the estimated number of errors in terms of false positives and false negatives.
