论文标题
半监督分类的增强学习
Augmentation Learning for Semi-Supervised Classification
论文作者
论文摘要
最近，出现了许多新的半监督学习方法。随着时间的推移，ImageNet和类似数据集的准确性随着时间的推移而提高，尚未探索自然图像分类以外的任务的性能。大多数半监督的学习方法都依赖于精心设计的数据增强管道，该数据不可传输用于在其他域的图像上学习。在这项工作中，我们提出了一种半监督的学习方法，该方法将自动为特定数据集选择最有效的数据增强策略。我们以FixMatch方法为基础，并通过增强的元学习来扩展它。在分类培训之前，在额外的培训中学习了增强，并利用双层优化，以优化增强策略并最大程度地提高准确性。我们在两个特定领域的数据集上评估了我们的方法，其中包含卫星图像和手绘草图，并获得最新的结果。我们在消融中进一步调查与学习增强策略相关的不同参数，并展示了如何使用策略学习将增强功能调整到ImageNet之外的数据集中。
Recently, a number of new Semi-Supervised Learning methods have emerged. As the accuracy for ImageNet and similar datasets increased over time, the performance on tasks beyond the classification of natural images is yet to be explored. Most Semi-Supervised Learning methods rely on a carefully manually designed data augmentation pipeline that is not transferable for learning on images of other domains. In this work, we propose a Semi-Supervised Learning method that automatically selects the most effective data augmentation policy for a particular dataset. We build upon the Fixmatch method and extend it with meta-learning of augmentations. The augmentation is learned in additional training before the classification training and makes use of bi-level optimization, to optimize the augmentation policy and maximize accuracy. We evaluate our approach on two domain-specific datasets, containing satellite images and hand-drawn sketches, and obtain state-of-the-art results. We further investigate in an ablation the different parameters relevant for learning augmentation policies and show how policy learning can be used to adapt augmentations to datasets beyond ImageNet.
