论文标题
遍历高斯进化过程的指导学习
Epoch-evolving Gaussian Process Guided Learning
论文作者
论文摘要
在本文中，我们提出了一种新颖的学习方案，称为Epoch-Evolving高斯过程指导学习（GPGL），该方案旨在表征批处理级分布与全球数据分布之间的相关信息。此类相关信息被编码为上下文标签，并且需要每个时期更新。在上下文标签和地面真相标签的指导下，GPGL方案通过更新三角形一致性损失的模型参数提供了更有效的优化。此外，我们的GPGL方案可以进一步概括，并自然应用于当前的深层模型，在主流数据集（CIFAR-10，CIFAR-100和TINY-IMBENET）上的现有基于批处理的最先进模型非常优于现有的基于批处理的最先进模型。
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.
