论文标题
NP匹配：当神经过程符合半监督学习时
NP-Match: When Neural Processes meet Semi-Supervised Learning
论文作者
论文摘要
近年来，半监督学习（SSL）已广泛探索，这是利用未标记数据来减少对标记数据的依赖的有效方法。在这项工作中，我们将神经过程（NP）调整为半监督图像分类任务，从而导致了一种名为NP匹配的新方法。 NP匹配适合此任务的原因有两个。首先，NP匹配在做出预测时隐含比较数据点，结果，每个未标记的数据点的预测都受到与之相似的标记数据点的影响，从而提高了伪标签的质量。其次，NP匹配能够估计不确定性，该不确定性可以用作选择具有可靠伪标签的未标记样品的工具。与使用Monte Carlo（MC）辍学实现的基于不确定性的SSL方法相比，NP匹配估计不确定性，计算开销较少，这可以节省训练和测试阶段的时间。我们在四个公共数据集上进行了广泛的实验，而NP匹配的表现优于最先进的结果（SOTA）或在其上取得了竞争成果，这表明了NP匹配的有效性及其对SSL的潜力。
Semi-supervised learning (SSL) has been widely explored in recent years, and it is an effective way of leveraging unlabeled data to reduce the reliance on labeled data. In this work, we adjust neural processes (NPs) to the semi-supervised image classification task, resulting in a new method named NP-Match. NP-Match is suited to this task for two reasons. Firstly, NP-Match implicitly compares data points when making predictions, and as a result, the prediction of each unlabeled data point is affected by the labeled data points that are similar to it, which improves the quality of pseudo-labels. Secondly, NP-Match is able to estimate uncertainty that can be used as a tool for selecting unlabeled samples with reliable pseudo-labels. Compared with uncertainty-based SSL methods implemented with Monte Carlo (MC) dropout, NP-Match estimates uncertainty with much less computational overhead, which can save time at both the training and the testing phases. We conducted extensive experiments on four public datasets, and NP-Match outperforms state-of-the-art (SOTA) results or achieves competitive results on them, which shows the effectiveness of NP-Match and its potential for SSL.
