We address the challenging problem of deep representation learning--the efficient adaption of a pre-trained deep network to different tasks. Specifically, we propose to explore gradient-based features. These features are gradients of the model parameters with respect to a task-specific loss given an input sample. Our key innovation is the design of a linear model that incorporates both gradient and activation of the pre-trained network. We show that our model provides a local linear approximation to an underlying deep model, and discuss important theoretical insights. Moreover, we present an efficient algorithm for the training and inference of our model without computing the actual gradient. Our method is evaluated across a number of representation-learning tasks on several datasets and using different network architectures. Strong results are obtained in all settings, and are well-aligned with our theoretical insights.

本文提出基于梯度的特征及使用线性模型将梯度与预训练网络的激活函数相结合用于深度表示学习，从而实现预训练网络对不同任务的高效适应，通过实验验证了该算法在不同网络结构下，在多个数据集的各种表征学习任务上获得了强大的结果并与理论分析相符。

梯度作为深度表示学习中的特征