  The objective of this paper is the effective transfer of the Convolutional
Neural Network (CNN) feature in image search and classification.
Systematically, we study three facts in CNN transfer. 1) We demonstrate the
advantage of using images with a properly large size as input to CNN instead of
the conventionally resized one. 2) We benchmark the performance of different
CNN layers improved by average/max pooling on the feature maps. Our observation
suggests that the Conv5 feature yields very competitive accuracy under such
pooling step. 3) We find that the simple combination of pooled features
extracted across various CNN layers is effective in collecting evidences from
both low and high level descriptors. Following these good practices, we are
capable of improving the state of the art on a number of benchmarks to a large
margin.
