  Deep neural networks are widely used in image classification problems.
However, little work addresses how features from different deep neural networks
affect the domain adaptation problem. Existing methods often extract deep
features from one ImageNet model, without exploring other neural networks. In
this paper, we investigate how different ImageNet models affect transfer
accuracy on domain adaptation problems. We extract features from sixteen
distinct pre-trained ImageNet models and examine the performance of twelve
benchmarking methods when using the features. Extensive experimental results
show that a higher accuracy ImageNet model produces better features, and leads
to higher accuracy on domain adaptation problems (with a correlation
coefficient of up to 0.95). We also examine the architecture of each neural
network to find the best layer for feature extraction. Together, performance
from our features exceeds that of the state-of-the-art in three benchmark
datasets.
