无需人工标注，用模型自身不确定性生成新样本提升学习效果。
OFAL: An Oracle-Free Active Learning Framework
- 用神经网络不确定性生成新样本，替代人工标注。
- 通过变分自编码器在隐空间中扩展不确定区域，生成高信息量样本。
- 可与主流主动学习方法结合，适合大规模无标签数据场景。
主动学习依赖人工标注（oracle）进行数据标记，成本高昂。本文提出OFAL框架，无需依赖人工标注，利用神经网络的不确定性生成高质量训练样本。首先，通过蒙特卡洛丢弃法近似贝叶斯神经网络，量化不同来源的不确定性；其次，引入变分自编码器，从高置信度样本出发，向隐空间中的不确定区域迁移，生成新的高信息量未标记样本；最后，将该方法与多种主流主动学习采样策略对比并融合。实验表明，该方法可在不依赖外部标注的情况下有效提升模型性能，适用于大规模无标签数据场景。
原文摘要 · Abstract (English)
In the active learning paradigm, using an oracle to label data has always been a complex and expensive task, and with the emersion of large unlabeled data pools, it would be highly beneficial If we could achieve better results without relying on an oracle. This research introduces OFAL, an oracle-free active learning scheme that utilizes neural network uncertainty. OFAL uses the model's own uncertainty to transform highly confident unlabeled samples into informative uncertain samples. First, we start with separating and quantifying different parts of uncertainty and introduce Monte Carlo Dropouts as an approximation of the Bayesian Neural Network model. Secondly, by adding a variational autoencoder, we go on to generate new uncertain samples by stepping toward the uncertain part of latent space starting from a confidence seed sample. By generating these new informative samples, we can perform active learning and enhance the model's accuracy. Lastly, we try to compare and integrate our method with other widely used active learning sampling methods.
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