  Convolutional Networks (ConvNets) are powerful models that learn hierarchies
of visual features, which could also be used to obtain image representations
for transfer learning. The basic pipeline for transfer learning is to first
train a ConvNet on a large dataset (source task) and then use feed-forward
units activation of the trained ConvNet as image representation for smaller
datasets (target task). Our key contribution is to demonstrate superior
performance of multiple ConvNet layer features over single ConvNet layer
features. Combining multiple ConvNet layer features will result in more complex
feature space with some features being repetitive. This requires some form of
feature selection. We use AdaBoost with single stumps to implicitly select only
distinct features that are useful towards classification from concatenated
ConvNet features. Experimental results show that using multiple ConvNet layer
activation features instead of single ConvNet layer features consistently will
produce superior performance. Improvements becomes significant as we increase
the distance between source task and the target task.
