论文标题
通过预期改善最大化在CNN中积极学习
Active Learning in CNNs via Expected Improvement Maximization
论文作者
论文摘要
诸如卷积神经网络（CNN）之类的深度学习模型在包括计算机视觉和最近的计算生物学等各种领域中表现出很高的有效性。但是，培训有效的模型通常需要组装和/或标记大型数据集，这可能是耗时或昂贵的。基于池的主动学习技术有可能减轻这些问题，利用对有限数据培训的模型，从池中选择性地查询未标记的数据点，以加快学习过程。在这里，我们介绍了“基于辍学的预期改进”（DEIMOS），这是一种灵活且计算上的积极学习方法，查询要点，这些方法有望在代表性点的代表性样本中最大程度地提高模型的改进。提出的框架使我们能够维护一个预测协方差矩阵捕获模型不确定性，并动态更新此矩阵，以便在批处理模式设置中生成各个点的批次。我们的积极学习结果表明，Deimos在从计算机视觉和基因组学上采取的多重回归和分类任务上优于现有基线。
Deep learning models such as Convolutional Neural Networks (CNNs) have demonstrated high levels of effectiveness in a variety of domains, including computer vision and more recently, computational biology. However, training effective models often requires assembling and/or labeling large datasets, which may be prohibitively time-consuming or costly. Pool-based active learning techniques have the potential to mitigate these issues, leveraging models trained on limited data to selectively query unlabeled data points from a pool in an attempt to expedite the learning process. Here we present "Dropout-based Expected IMprOvementS" (DEIMOS), a flexible and computationally-efficient approach to active learning that queries points that are expected to maximize the model's improvement across a representative sample of points. The proposed framework enables us to maintain a prediction covariance matrix capturing model uncertainty, and to dynamically update this matrix in order to generate diverse batches of points in the batch-mode setting. Our active learning results demonstrate that DEIMOS outperforms several existing baselines across multiple regression and classification tasks taken from computer vision and genomics.
