Does ResNet Learn Good General Purpose Features?
Yang Li, Yafei Zhang, Yulong Xu, Zhuang Miao, Hang Li · 2017
ResNet with hundreds or even thousands of layers has become the most successful image recognition model in the computer vision community. However, we do not know whether this deeper architecture has better transferability than the traditional models. In this paper, we systematically investigate the well-known ResNet features for different computer vision tasks without fine-tuning. The experimental results show that ResNet did not learn good general purpose features in image retrieval and visual object tracking tasks. GoogLeNet high-level features and VGGNet middle-level features achieve better performance for transfer learning. Therefore, it is not a good idea to choose ResNet model in other computer vision tasks without fine-tuning.