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A Neural Pre-Conditioning Active Learning Algorithm to Reduce Label Complexity
Although unlabeled data is readily available in pool-based AL, AL algorithms are usually evaluated by measuring the increase in supervised learning (SL) performance at consecutive acquisition steps.
Better Optimization can Reduce Sample Complexity: Active Semi-Supervised Learning via Convergence Rate Control
We name this AL scheme convergence rate control (CRC), and our experiments show that a deep neural network trained using a combination of CRC and a recently proposed SSL algorithm can quickly achieve high performance using far less labeled samples than SL.
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