Low-Cost Transfer Learning of Face Tasks
Do we know what the different filters of a face network 
represent? Can we use this filter information to train other 
tasks without transfer learning? For instance, can age, head 
pose, emotion and other face related tasks be learned from 
face recognition network without transfer learning? Understanding the role of these filters allows us to transfer 
knowledge across tasks and take advantage of large data 
sets in related tasks. Given a pretrained network, we can 
infer which tasks the network generalizes for and the best 
way to transfer the information to a new task. 
We demonstrate a computationally inexpensive algorithm to reuse the filters of a face network for a task it was 
not trained for. Our analysis proves these attributes can be 
extracted with an accuracy comparable to what is obtained 
with transfer learning, but 10 times faster. We show that the 
information about other tasks is present in relatively small 
number of filters. We use these insights to do task specific 
pruning of a pretrained network. Our method gives significant compression ratios with reduction in size of 95% and 
computational reduction of 60%