In this paper, we propose a novel learning scheme called epoch-evolving Gaussian Process Guided Learning (GPGL), which aims at characterizing the correlation information between the batch-level distribution and the global data distribution. Such correlation information is encoded as context labels and needs renewal every epoch. With the guidance of the context label and ground truth label, GPGL scheme provides a more efficient optimization through updating the model parameters with a triangle consistency loss. Furthermore, our GPGL scheme can be further generalized and naturally applied to the current deep models, outperforming the existing batch-based state-of-the-art models on mainstream datasets (CIFAR-10, CIFAR-100, and Tiny-ImageNet) remarkably.

翻译：本文提出一种名为"基于epoch演变的高斯过程引导学习"（GPGL）的新型学习框架，旨在刻画批次级分布与全局数据分布之间的相关性信息。该相关性信息以上下文标签形式编码，并需在每个训练周期进行更新。在上下文标签与真实标签的引导下，GPGL框架通过引入三角一致性损失函数更新模型参数，从而实现更高效的优化过程。此外，本文所提出的GPGL框架可进一步泛化并自然应用于现有深度模型，在主流数据集（CIFAR-10、CIFAR-100和Tiny-ImageNet）上的性能显著优于当前基于批次的先进模型。