论文标题
可视化转移学习
Visualizing Transfer Learning
论文作者
论文摘要
在转移学习的时间过程中，我们提供了深层图像识别网络的单个神经元的可视化。这些可视化质量地证明了转移学习过程的各种新型特性，涉及适应性的速度和特征，神经元重复使用，代表图像特征的空间尺度以及传递学习到小数据的行为。我们发布了为此分析目的创建的大规模数据集。
We provide visualizations of individual neurons of a deep image recognition network during the temporal process of transfer learning. These visualizations qualitatively demonstrate various novel properties of the transfer learning process regarding the speed and characteristics of adaptation, neuron reuse, spatial scale of the represented image features, and behavior of transfer learning to small data. We publish the large-scale dataset that we have created for the purposes of this analysis.
