论文标题
一声半监督学习的经验观点
Empirical Perspectives on One-Shot Semi-supervised Learning
论文作者
论文摘要
采用深神经网络为新应用程序采用的最大障碍之一是训练网络通常需要大量手动标记的培训样品。我们从经验上研究了一个方案，其中人们可以访问大量未标记的数据，但需要每类单个原型样本标签才能训练一个深层的网络（即，一击半监督学习）。具体而言，我们调查了FixMatch报告的最新结果，以了解一张半监督的学习，以了解影响和阻碍高准确性和可靠性的因素，并为CIFAR-10的一次性半监督学习。例如，我们发现，对高性能图像分类的单发半监督学习的一个障碍是培训期间班级准确性的不均匀性。这些结果指出的是解决方案，这些解决方案可能会更广泛地采用针对新应用程序的单发半监督培训方法。
One of the greatest obstacles in the adoption of deep neural networks for new applications is that training the network typically requires a large number of manually labeled training samples. We empirically investigate the scenario where one has access to large amounts of unlabeled data but require labeling only a single prototypical sample per class in order to train a deep network (i.e., one-shot semi-supervised learning). Specifically, we investigate the recent results reported in FixMatch for one-shot semi-supervised learning to understand the factors that affect and impede high accuracies and reliability for one-shot semi-supervised learning of Cifar-10. For example, we discover that one barrier to one-shot semi-supervised learning for high-performance image classification is the unevenness of class accuracy during the training. These results point to solutions that might enable more widespread adoption of one-shot semi-supervised training methods for new applications.
