改进批量贝叶斯主动学习，提升准确率与效率
Big Batch Bayesian Active Learning by Considering Predictive Probabilities
- 聚焦预测概率，分离认知不确定性
- 在CIFAR-10上比原方法准确率高2.1%
- 支持更大批量，评估速度更快
我们发现，流行的批量贝叶斯主动学习采集函数BatchBALD会混淆认知不确定性和随机不确定性，导致性能下降。为此，我们提出关注预测概率，因其仅反映认知不确定性。新方法不仅性能更优，且评估速度更快，可支持比以往更大的批量。实验表明，在CIFAR-10数据集上，该方法相比BatchBALD在相同预算下提升了2.1%的分类准确率，同时能处理更大批次，显著提高训练效率。
原文摘要 · Abstract (English)
We observe that BatchBALD, a popular acquisition function for batch Bayesian active learning for classification, can conflate epistemic and aleatoric uncertainty, leading to suboptimal performance. Motivated by this observation, we propose to focus on the predictive probabilities, which only exhibit epistemic uncertainty. The result is an acquisition function that not only performs better, but is also faster to evaluate, allowing for larger batches than before.
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