This paper is about regularizing deep convolutional networks (CNNs) based on
an adaptive framework for transfer learning with limited training data in the
target domain. Recent advances of CNN regularization in this context are
commonly due to the use of additional regularization objectives. They guide the
training away from the target task using some forms of concrete tasks. Unlike
those related approaches, we suggest that an objective without a concrete goal
can still serve well as a regularized. In particular, we demonstrate
Pseudo-task Regularization (PtR) which dynamically regularizes a network by
simply attempting to regress image representations to pseudo-regression targets
during fine-tuning. That is, a CNN is efficiently regularized without
additional resources of data or prior domain expertise. In sum, the proposed
PtR provides: a) an alternative for network regularization without dependence
on the design of concrete regularization objectives or extra annotations; b) a
dynamically adjusted and maintained strength of regularization effect by
balancing the gradient norms between objectives on-line. Through numerous
experiments, surprisingly, the improvements on classification accuracy by PtR
are shown greater or on a par to the recent state-of-the-art methods.

该论文提出了一种基于自适应框架的 CNN 正则化方法，在目标领域使用有限的训练数据进行迁移学习。该方法没有明确目标，通过简单地尝试将图像表示回归到伪回归目标，动态地对 CNN 进行正则化。实验结果表明，与最近的一些最先进的方法相比，伪任务正则化改进了分类精度。