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Diverse mini-batch Active Learning

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arxiv 1901.05954 v1 pith:JTDCA3IS submitted 2019-01-17 cs.LG stat.ML

Fedor Zhdanov This is my paper · ORCID
classification cs.LGstat.ML
keywords exampleslearningactiveapproachmini-batchamountbettermodel
verification ladder T0 review T1 audit T2 compute T3 formal
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We study the problem of reducing the amount of labeled training data required to train supervised classification models. We approach it by leveraging Active Learning, through sequential selection of examples which benefit the model most. Selecting examples one by one is not practical for the amount of training examples required by the modern Deep Learning models. We consider the mini-batch Active Learning setting, where several examples are selected at once. We present an approach which takes into account both informativeness of the examples for the model, as well as the diversity of the examples in a mini-batch. By using the well studied K-means clustering algorithm, this approach scales better than the previously proposed approaches, and achieves comparable or better performance.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Uncertainty Herding: One Active Learning Method for All Label Budgets

    cs.LG 2024-12 conditional novelty 6.0 of 10

    Uncertainty Herding weights coverage by model uncertainty and adapts two parameters so it matches or beats specialized active learning methods at both low and high label budgets.

  2. Label Distribution Learning using the Squared Neural Family on the Probability Simplex

    cs.LG 2024-12 conditional novelty 6.0 of 10

    SNEFY-LDL models the conditional distribution of label distribution vectors on the simplex using the Squared Neural Family, with closed-form mean, variance and covariance.

  3. PromptAL: Sample-Aware Dynamic Soft Prompts for Few-Shot Active Learning

    cs.CL 2025-07 conditional novelty 5.0 of 10

    PromptAL combines sample-aware dynamic soft prompts with uncertainty and diversity scores to select better annotation candidates in few-shot active learning.

  4. Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data

    eess.SP 2024-12 reject novelty 4.0 of 10

    Dense Poisson Disk Sampling before adaptive fine-tuning improved P300 BCI accuracy by about 5 percentage points and cut training time by 61%, but the evaluation protocol makes the gain unreliable.

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