We study the intrinsic transformation of feature maps across convolutional
network layers with explicit top-down control. To this end, we develop top-down
feature transformer (TFT), under controllable parameters, that are able to
account for the hidden layer transformation while maintaining the overall
consistency across layers...
The learned generators capture the underlying
feature transformation processes that are independent of particular training
images. Our proposed TFT framework brings insights to and helps the
understanding of, an important problem of studying the CNN internal feature
representation and transformation under the top-down processes. In the case of
spatial transformations, we demonstrate the significant advantage of TFT over
existing data-driven approaches in building data-independent transformations. We also show that it can be adopted in other applications such as data
augmentation and image style transfer.